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Fusco R, Granata V, Setola SV, Trovato P, Galdiero R, Mattace Raso M, Maio F, Porto A, Pariante P, Cerciello V, Sorgente E, Pecori B, Castaldo M, Izzo F, Petrillo A. The application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer: A systematic review. Phys Med 2025; 130:104891. [PMID: 39787678 DOI: 10.1016/j.ejmp.2025.104891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2024] [Revised: 12/31/2024] [Accepted: 01/02/2025] [Indexed: 01/12/2025] Open
Abstract
PURPOSE To study the application of radiomics in cancer imaging with a focus on lung cancer, renal cell carcinoma, gastrointestinal cancer, and head and neck cancer. METHODS Different electronic databases were considered. Articles published in the last five years were analyzed (January 2019 and December 2023). Papers were selected by two investigators with over 15 years of experience in Radiomics analysis in cancer imaging. The methodological quality of each radiomics study was performed using the Radiomic Quality Score (RQS) by two different readers in consensus and then by a third operator to solve disagreements between the two readers. RESULTS 19 articles are included in the review. Among the analyzed studies, only one study achieved an RQS of 18 reporting multivariable analyzes with also non-radiomics features and using the validation phase considering two datasets from two distinct institutes and open science and data domain. CONCLUSION This informative review has brought attention to the increasingly consolidated potential of Radiomics, although there are still several aspects to be evaluated before the transition to routine clinical practice. There are several challenges to address, including the need for standardization at all stages of the workflow and the potential for cross-site validation using heterogeneous real-world datasets. It will be necessary to establish and promote an imaging data acquisition protocol, conduct multicenter prospective quality control studies, add scanner differences and vendor-dependent characteristics; to collect images of individuals at additional time points, to report calibration statistics.
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Affiliation(s)
- Roberta Fusco
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy.
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Piero Trovato
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Mauro Mattace Raso
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Francesca Maio
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Annamaria Porto
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Paolo Pariante
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Vincenzo Cerciello
- Division of Health Physics, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Eugenio Sorgente
- Division of Radiation protection and innovative technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Biagio Pecori
- Division of Radiation protection and innovative technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Mimma Castaldo
- Unit of "Progettazione e Manutenzione Edile ed impianti", Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale, IRCCS di Napoli, 80131 Naples, Italy
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Chen JL, Chen YS, Hsieh KC, Lee HM, Chen CY, Chen JH, Hung CM, Hsu CT, Huang YL, Ker CG. Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1410. [PMID: 39336451 PMCID: PMC11433876 DOI: 10.3390/medicina60091410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/09/2024] [Accepted: 08/21/2024] [Indexed: 09/30/2024]
Abstract
Background and Objectives: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. Materials and Methods: A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012-2015 formed the derivation cohort, and those from 2016-2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. Results: Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(-) and 80.0%, 71.8%, and 41.2% for MVI(+) (p < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. Conclusions: Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible.
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Affiliation(s)
- Jen-Lung Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Yaw-Sen Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Kun-Chou Hsieh
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Hui-Ming Lee
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Chung-Yen Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Jian-Han Chen
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
| | - Chao-Ming Hung
- Department of General Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Chao-Tien Hsu
- Department of Pathology, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Ya-Ling Huang
- Cancer Registration Center, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan;
| | - Chen-Guo Ker
- Department of General Surgery, E-Da Hospital, I-Shou University, Kaohsiung 824, Taiwan; (J.-L.C.); (Y.-S.C.); (K.-C.H.); (H.-M.L.); (C.-Y.C.); (J.-H.C.)
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Chen L, Yin G, Wang Z, Liu Z, Sui C, Chen K, Song T, Xu W, Qi L, Li X. A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma. Biosci Trends 2024; 18:263-276. [PMID: 38853000 DOI: 10.5582/bst.2024.01121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.
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Affiliation(s)
- Lu Chen
- Department of Hepatobiliary Cancer, Liver Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Guotao Yin
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Zifan Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Tianqiang Song
- Department of Hepatobiliary Cancer, Liver Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Lisha Qi
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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Li SQ, Yang CX, Wu CM, Cui JJ, Wang JN, Yin XP. Prediction of glypican-3 expression in hepatocellular carcinoma using multisequence magnetic resonance imaging-based histology nomograms. Quant Imaging Med Surg 2024; 14:4436-4449. [PMID: 39022267 PMCID: PMC11250339 DOI: 10.21037/qims-24-111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 05/11/2024] [Indexed: 07/20/2024]
Abstract
Background Hepatocellular carcinoma (HCC) is often associated with the overexpression of multiple proteins and genes. For instance, patients with HCC and a high expression of the glypican-3 (GPC3) gene have a poor prognosis, and noninvasive assessment of GPC3 expression before surgery is helpful for clinical decision-making. Therefore, our primary aim in this study was to develop and validate multisequence magnetic resonance imaging (MRI) radiomics nomograms for predicting the expression of GPC3 in individuals diagnosed with HCC. Methods We conducted a retrospective analysis of 143 patients with HCC, including 123 cases from our hospital and 20 cases from The Cancer Genome Atlas (TCGA) or The Cancer Imaging Archive (TCIA) public databases. We used preoperative multisequence MRI images of the patients for the radiomics analysis. We extracted and screened the imaging histologic features using fivefold cross-validation, Pearson correlation coefficient, and the least absolute shrinkage and selection operator (LASSO) analysis method. We used logistic regression (LR) to construct a radiomics model, developed nomograms based on the radiomics scores and clinical parameters, and evaluated the predictive performance of the nomograms using receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results Our multivariate analysis results revealed that tumor morphology (P=0.015) and microvascular (P=0.007) infiltration could serve as independent predictors of GPC3 expression in patients with HCC. The nomograms integrating multisequence radiomics radiomics score, tumor morphology, and microvascular invasion had an area under the curve (AUC) value of 0.989. This approach was superior to both the radiomics model (AUC 0.979) and the clinical model (AUC 0.793). The sensitivity, specificity, and accuracy of 0.944, 0.800, and 0.913 for the test set, respectively, and the model's calibration curve demonstrated good consistency (Brier score =0.029). The decision curve analysis (DCA) indicated that the nomogram had a higher net clinical benefit for predicting the expression of GPC3. External validation of the model's prediction yielded an AUC value of 0.826. Conclusions Our study findings highlight the close association of multisequence MRI imaging and radiomic features with GPC3 expression. Incorporating clinical parameters into nomograms can offer valuable preoperative insights into tailoring personalized treatment plans for patients diagnosed with HCC.
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Affiliation(s)
- Si-Qi Li
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Cun-Xia Yang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Chun-Mei Wu
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Jing-Jing Cui
- United Imaging Intelligence (Beijing) Co., Ltd., Beijing, China
| | - Jia-Ning Wang
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
| | - Xiao-Ping Yin
- Department of Radiology, Affiliated Hospital of Hebei University, Baoding, China
- College of Clinical Medical of Hebei University, Baoding, China
- Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, Baoding, China
- College of Nursing of Hebei University, Baoding, China
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Ballı HT, Pişkin FC, Püren Yücel S, Sözütok S, Özgül D, Aikimbaev K. Predictability of the radiological response to Yttrium-90 transarterial radioembolization by dynamic magnetic resonance imaging-based radiomics analysis in patients with intrahepatic cholangiocarcinoma. Diagn Interv Radiol 2024; 30:193-199. [PMID: 36994655 PMCID: PMC11095063 DOI: 10.4274/dir.2023.222025] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/24/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE The study aims to investigate the predictability of the radiological response in intrahepatic cholangiocarcinoma (iCC) patients undergoing Yttrium-90 transarterial radioembolization (TARE) with a combined model built on dynamic magnetic resonance imaging (MRI)-based radiomics and clinical features. METHODS Thirty-six naive iCC patients who underwent TARE were included in this study. The tumor segmentation was performed on the axial T2-weighted (T2W) without fat suppression, axial T2W with fat suppression, and axial T1-weighted (T1W) contrast-enhanced (CE) sequence in equilibrium phase (Eq). At the sixth month MRI follow-up, all patients were divided into responders and non-responders according to the modified Response Evaluation Criteria in Solid Tumors. Subsequently, a radiomics score (rad-score) and a combined model of the rad-score and clinical features for each sequence were generated and compared between the groups. RESULTS Thirteen (36.1%) patients were considered responders, and the remaining 23 (63.9%) were non-responders. Responders exhibited significantly lower rad-scores than non-responders (P < 0.050 for all sequences). The radiomics models showed good discriminatory ability with an area under the curve (AUC) of 0.696 [95% confidence interval (CI), 0.522–0.870] for the axial T1W-CE-Eq, AUC of 0.839 (95% CI, 0.709–0.970) for the axial T2W with fat suppression, and AUC of 0.836 (95% CI, 0.678–0.995) for the axial T2W without fat suppression. CONCLUSION Radiomics models created by pre-treatment MRIs can predict the radiological response to Yttrium- 90 TARE in iCC patients with high accuracy. Combining radiomics with clinical features could increase the power of the test. Large-scale studies of multi-parametric MRIs with internal and external validations are needed to determine the clinical value of radiomics in iCC patients.
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Affiliation(s)
- Hüseyin Tuğsan Ballı
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Radiology, Adana, Türkiye
| | - Ferhat Can Pişkin
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Radiology, Adana, Türkiye
| | - Sevinç Püren Yücel
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Biostatistics, Adana, Türkiye
| | - Sinan Sözütok
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Radiology, Adana, Türkiye
| | - Duygu Özgül
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Radiology, Adana, Türkiye
| | - Kairgeldy Aikimbaev
- Çukurova University Faculty of Medicine, Balçalı Hospital Health Application and Research Center, Department of Radiology, Adana, Türkiye
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Starmans MPA, Miclea RL, Vilgrain V, Ronot M, Purcell Y, Verbeek J, Niessen WJ, Ijzermans JNM, de Man RA, Doukas M, Klein S, Thomeer MG. Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics. Acad Radiol 2024; 31:870-879. [PMID: 37648580 DOI: 10.1016/j.acra.2023.07.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/06/2023] [Accepted: 07/25/2023] [Indexed: 09/01/2023]
Abstract
RATIONALE AND OBJECTIVES Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers. MATERIALS AND METHODS Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018. Patients with malignant (hepatocellular carcinoma and intrahepatic cholangiocarcinoma) and benign (hepatocellular adenoma and focal nodular hyperplasia) lesions were included. A radiomics model based on T2-weighted MRI was developed in data set A using a combination of machine learning approaches. The model was internally evaluated on data set A through cross-validation, externally validated on data sets B and C, and compared to visual scoring of two experienced abdominal radiologists on data set C. RESULTS The overall data set included 486 patients (A: 187, B: 98, and C: 201). The radiomics model had a mean area under the curve (AUC) of 0.78 upon internal validation on data set A and a similar AUC in external validation (B: 0.74 and C: 0.76). In data set C, the two radiologists showed moderate agreement (Cohen's κ: 0.61) and achieved AUCs of 0.86 and 0.82. CONCLUSION Our T2-weighted MRI radiomics model shows potential for distinguishing malignant from benign primary solid liver lesions. External validation indicated that the model is generalizable despite substantial MRI acquisition protocol differences. Pending further optimization and generalization, this model may aid radiologists in improving the diagnostic workup of patients with liver lesions.
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Affiliation(s)
- Martijn P A Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.).
| | - Razvan L Miclea
- Department of Radiology and Nuclear Medicine, Maastricht UMC+, Maastricht, the Netherlands (R.L.M.)
| | - Valerie Vilgrain
- Université de Paris, INSERM U 1149, CRI, Paris, France (V.V., M.R.); Département de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France (V.V., M.R.)
| | - Maxime Ronot
- Université de Paris, INSERM U 1149, CRI, Paris, France (V.V., M.R.); Département de Radiologie, Hôpital Beaujon, APHP.Nord, Clichy, France (V.V., M.R.)
| | - Yvonne Purcell
- Department of Radiology, Hôpital Fondation Rothschild, Paris, France (Y.P.)
| | - Jef Verbeek
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium (J.V.); Department of Gastroenterology and Hepatology, Maastricht UMC+, Maastricht, the Netherlands (J.V.)
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.); Faculty of Applied Sciences, Delft University of Technology, the Netherlands (W.J.N.)
| | - Jan N M Ijzermans
- Department of Surgery, Erasmus MC, Rotterdam, the Netherlands (J.N.M.I.)
| | - Rob A de Man
- Department of Gastroenterology & Hepatology, Erasmus MC, Rotterdam, the Netherlands (R.A.d.M.)
| | - Michael Doukas
- Department of Pathology, Erasmus MC, Rotterdam, the Netherlands (M.D.)
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.)
| | - Maarten G Thomeer
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, the Netherlands (M.P.A.S., W.J.N., S.K., M.G.T.)
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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8
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Wang C, Chen J, Zheng N, Zheng K, Zhou L, Zhang Q, Zhang W. Predicting the risk of distant metastasis in patients with locally advanced rectal cancer using model based on pre-treatment T2WI-based radiomic features plus postoperative pathological stage. Front Oncol 2023; 13:1109588. [PMID: 37746305 PMCID: PMC10517628 DOI: 10.3389/fonc.2023.1109588] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/17/2023] [Indexed: 09/26/2023] Open
Abstract
Objective To assess the prognostic value of a model based on pre-treatment T2WI-based radiomic features and postoperative pathological staging in patients with locally advanced rectal cancer who have undergone neoadjuvant chemoradiotherapy. Methods Radiomic features were derived from T2WI, and a radiomic signature (RS) was established and validated for the prediction of distant metastases (DM). Subsequently, we designed and validated a nomogram model that combined the radiomic signature and postoperative pathological staging for enhanced DM prediction. Performance measures such as the concordance index (C-index) and area under the curve (AUC) were computed to assess the predictive accuracy of the models. Results A total of 260 patients participated in this study, of whom 197 (75.8%) were male, and the mean age was 57.2 years with a standard deviation of 11.2 years. 15 radiomic features were selected to define the radiomic signature. Patients with a high-risk radiomic signature demonstrated significantly shorter distant metastasis-free survival (DMFS) in both the development and validation cohorts. A nomogram, incorporating the radiomic signature, pathological T stage, and N stage, achieved an area under the curve (AUC) value of 0.72 (95% CI, 0.60-0.83) in the development cohort and 0.83 (95% CI, 0.73-0.92) in the validation cohort. Conclusion A radiomic signature derived from T2WI-based radiomic features can effectively distinguish patients with varying risks of DM. Furthermore, a nomogram integrating the radiomic signature and postoperative pathological stage proves to be a robust predictor of DMFS.
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Affiliation(s)
- Chen Wang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Jingjing Chen
- Graduate School of Naval Medical University, Shanghai, China
| | - Nanxin Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Kuo Zheng
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Lu Zhou
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
| | - Qianwen Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wei Zhang
- Department of Colorectal Surgery, Changhai Hospital, Naval Medical University, Shanghai, China
- Hereditary Colorectal Cancer Center and Genetic Block Center of Familial Cancer, Changhai Hospital, Shanghai, China
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9
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Franco D, Granata V, Fusco R, Grassi R, Nardone V, Lombardi L, Cappabianca S, Conforti R, Briganti F, Grassi R, Caranci F. Artificial intelligence and radiation effects on brain tissue in glioblastoma patient: preliminary data using a quantitative tool. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01655-0. [PMID: 37289266 DOI: 10.1007/s11547-023-01655-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/09/2023]
Abstract
PURPOSE The quantification of radiotherapy (RT)-induced functional and morphological brain alterations is fundamental to guide therapeutic decisions in patients with brain tumors. The magnetic resonance imaging (MRI) allows to define structural RT-brain changes, but it is unable to evaluate early injuries and to objectively quantify the volume tissue loss. Artificial intelligence (AI) tools extract accurate measurements that permit an objective brain different region quantification. In this study, we assessed the consistency between an AI software (Quibim Precision® 2.9) and qualitative neruroradiologist evaluation, and its ability to quantify the brain tissue changes during RT treatment in patients with glioblastoma multiforme (GBM). METHODS GBM patients treated with RT and subjected to MRI assessment were enrolled. Each patient, pre- and post-RT, undergoes to a qualitative evaluation with global cerebral atrophy (GCA) and medial temporal lobe atrophy (MTA) and a quantitative assessment with Quibim Brain screening and hippocampal atrophy and asymmetry modules on 19 extracted brain structures features. RESULTS A statistically significant strong negative association between the percentage value of the left temporal lobe and the GCA score and the left temporal lobe and the MTA score was found, while a moderate negative association between the percentage value of the right hippocampus and the GCA score and the right hippocampus and the MTA score was assessed. A statistically significant strong positive association between the CSF percentage value and the GCA score and a moderate positive association between the CSF percentage value and the MTA score was found. Finally, quantitative feature values showed that the percentage value of the cerebro-spinal fluid (CSF) statistically differences between pre- and post-RT. CONCLUSIONS AI tools can support a correct evaluation of RT-induced brain injuries, allowing an objective and earlier assessment of the brain tissue modifications.
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Affiliation(s)
- Donatella Franco
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Research & Development and Medical Oncology Division, Igea SpA, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122, Milan, Italy
| | - Valerio Nardone
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Laura Lombardi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Salvatore Cappabianca
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Renata Conforti
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Francesco Briganti
- Advanced Biomedical Sciences Department, Federico II University, Naples, Italy
| | - Roberto Grassi
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Ferdinando Caranci
- Division of Radiology, Department of Precision Medicine, "Università degli Studi della Campania Luigi Vanvitelli", Naples, Italy
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10
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Patrone R, Ottaiano A, Nasti G, Silvestro L, Cassata A, Grassi F, Avallone A, Izzo F, Petrillo A. Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics. Infect Agent Cancer 2023; 18:18. [PMID: 36927442 PMCID: PMC10018963 DOI: 10.1186/s13027-023-00495-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/07/2023] [Indexed: 03/18/2023] Open
Abstract
In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy.
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, Napoli, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, Milan, 20122, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", Bari, 70124, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Alessandro Ottaiano
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Guglielmo Nasti
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Lucrezia Silvestro
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Antonio Cassata
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesca Grassi
- Division of Radiology, "Università degli Studi della Campania Luigi Vanvitelli", Naples, 80138, Italy
| | - Antonio Avallone
- Clinical Sperimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, Napoli, 80131, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, Naples, 80131, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Naples, Italy
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11
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Gabelloni M, Faggioni L, Fusco R, De Muzio F, Danti G, Grassi F, Grassi R, Palumbo P, Bruno F, Borgheresi A, Bruno A, Catalano O, Gandolfo N, Giovagnoni A, Miele V, Barile A, Granata V. Exploring Radiologists' Burnout in the COVID-19 Era: A Narrative Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3350. [PMID: 36834044 PMCID: PMC9966123 DOI: 10.3390/ijerph20043350] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/03/2023] [Accepted: 02/12/2023] [Indexed: 06/18/2023]
Abstract
Since its beginning in March 2020, the COVID-19 pandemic has claimed an exceptionally high number of victims and brought significant disruption to the personal and professional lives of millions of people worldwide. Among medical specialists, radiologists have found themselves at the forefront of the crisis due to the pivotal role of imaging in the diagnostic and interventional management of COVID-19 pneumonia and its complications. Because of the disruptive changes related to the COVID-19 outbreak, a proportion of radiologists have faced burnout to several degrees, resulting in detrimental effects on their working activities and overall wellbeing. This paper aims to provide an overview of the literature exploring the issue of radiologists' burnout in the COVID-19 era.
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Affiliation(s)
- Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research, University of Pisa, 56126 Pisa, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy
| | - Ginevra Danti
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
| | - Francesca Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, Università degli Studi della Campania “Luigi Vanvitelli”, 80138 Naples, Italy
| | - Pierpaolo Palumbo
- Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Department of Diagnostic Imaging, 67100 L’Aquila, Italy
| | - Federico Bruno
- Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, Department of Diagnostic Imaging, 67100 L’Aquila, Italy
| | - Alessandra Borgheresi
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Alessandra Bruno
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Orlando Catalano
- Department of Radiology, Istituto Diagnostico Varelli, 80126 Naples, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, 16149 Genoa, Italy
| | - Andrea Giovagnoni
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, 60126 Ancona, Italy
- Department of Clinical, Special and Dental Sciences, Università Politecnica delle Marche, 60126 Ancona, Italy
| | - Vittorio Miele
- Department of Emergency Radiology, Careggi University Hospital, 50134 Florence, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, 20122 Milan, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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12
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Granata V, Fusco R, De Muzio F, Cutolo C, Grassi F, Brunese MC, Simonetti I, Catalano O, Gabelloni M, Pradella S, Danti G, Flammia F, Borgheresi A, Agostini A, Bruno F, Palumbo P, Ottaiano A, Izzo F, Giovagnoni A, Barile A, Gandolfo N, Miele V. Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence. BIOLOGY 2023; 12:213. [PMID: 36829492 PMCID: PMC9952965 DOI: 10.3390/biology12020213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 01/21/2023] [Accepted: 01/25/2023] [Indexed: 01/31/2023]
Abstract
Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy
| | - Federica De Muzio
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Salerno, Italy
| | - Francesca Grassi
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80138 Naples, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Orlando Catalano
- Radiology Unit, Istituto Diagnostico Varelli, Via Cornelia dei Gracchi 65, 80126 Naples, Italy
| | - Michela Gabelloni
- Nuclear Medicine Unit, Department of Translational Research, University of Pisa, 56216 Pisa, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Ginevra Danti
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Federica Flammia
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
| | - Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Federico Bruno
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Pierpaolo Palumbo
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Alessandro Ottaiano
- SSD Innovative Therapies for Abdominal Metastases, Istituto Nazionale Tumori IRCCS-Fondazione G. Pascale, 80130 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, Via Conca 71, 60126 Ancona, Italy
- Department of Radiology, University Hospital “Azienda Ospedaliera Universitaria delle Marche”, Via Conca 71, 60126 Ancona, Italy
| | - Antonio Barile
- Department of Applied Clinical Sciences and Biotechnology, University of L’Aquila, Via Vetoio 1, 67100 L’Aquila, Italy
| | - Nicoletta Gandolfo
- Diagnostic Imaging Department, Villa Scassi Hospital-ASL 3, Corso Scassi 1, 16149 Genoa, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134 Florence, Italy
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13
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Hu X, Li C, Wang Q, Wu X, Chen Z, Xia F, Cai P, Zhang L, Fan Y, Ma K. Development and External Validation of a Radiomics Model Derived from Preoperative Gadoxetic Acid-Enhanced MRI for Predicting Histopathologic Grade of Hepatocellular Carcinoma. Diagnostics (Basel) 2023; 13:diagnostics13030413. [PMID: 36766518 PMCID: PMC9914153 DOI: 10.3390/diagnostics13030413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/19/2023] [Accepted: 01/19/2023] [Indexed: 01/24/2023] Open
Abstract
Histopathologic grade of hepatocellular carcinoma (HCC) is an important predictor of early recurrence and poor prognosis after curative treatments. This study aims to develop a radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting HCC histopathologic grade and to validate its predictive performance in an independent external cohort. Clinical and imaging data of 403 consecutive HCC patients were retrospectively collected from two hospitals (265 and 138, respectively). Patients were categorized into poorly differentiated HCC and non-poorly differentiated HCC groups. A total of 851 radiomics features were extracted from the segmented tumor at the hepatobiliary phase images. Three classifiers, logistic regression (LR), support vector machine, and Adaboost were adopted for modeling. The areas under the curve of the three models were 0.70, 0.67, and 0.61, respectively, in the external test cohort. Alpha-fetoprotein (AFP) was the only significant clinicopathological variable associated with HCC grading (odds ratio: 2.75). When combining AFP, the LR+AFP model showed the best performance, with an AUC of 0.71 (95%CI: 0.59-0.82) in the external test cohort. A radiomics model based on gadoxetic acid-enhanced MRI was constructed in this study to discriminate HCC with different histopathologic grades. Its good performance indicates a promise in the preoperative prediction of HCC differentiation levels.
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Affiliation(s)
- Xiaojun Hu
- The Department of General Surgery & Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
- Department of Hepatobiliary Surgery, The Fifth Affiliated Hospital of Southern Medical University, Guangzhou 510920, China
| | - Changfeng Li
- Institution of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Qiang Wang
- Division of Medical Imaging and Technology, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, 14152 Stockholm, Sweden
- Department of Radiology, Karolinska University Hospital Huddinge, 14186 Stockholm, Sweden
| | - Xueyun Wu
- The Department of General Surgery & Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
| | - Zhiyu Chen
- Institution of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Feng Xia
- Institution of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Ping Cai
- Department of Radiology, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Leida Zhang
- Institution of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
| | - Yingfang Fan
- The Department of General Surgery & Hepatobiliary Surgery, Zhujiang Hospital, Southern Medical University, Guangzhou 510280, China
- Correspondence: (Y.F.); (K.M.); Tel.: +86-20-62782567 (Y.F.); +86-15-213-249-505 (K.M.)
| | - Kuansheng Ma
- Institution of Hepatobiliary Surgery, Southwest Hospital, Army Medical University, Chongqing 400038, China
- Correspondence: (Y.F.); (K.M.); Tel.: +86-20-62782567 (Y.F.); +86-15-213-249-505 (K.M.)
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14
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Granata V, Fusco R, Setola SV, Galdiero R, Maggialetti N, Silvestro L, De Bellis M, Di Girolamo E, Grazzini G, Chiti G, Brunese MC, Belli A, Patrone R, Palaia R, Avallone A, Petrillo A, Izzo F. Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence. Cancers (Basel) 2023; 15:351. [PMID: 36672301 PMCID: PMC9857317 DOI: 10.3390/cancers15020351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/30/2022] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 41012 Napoli, Italy
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Roberta Galdiero
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari “Aldo Moro”, 70124 Bari, Italy
| | - Lucrezia Silvestro
- Division of Clinical Experimental Oncology Abdomen, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Mario De Bellis
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Elena Di Girolamo
- Division of Gastroenterology and Digestive Endoscopy, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Giulia Grazzini
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Giuditta Chiti
- Department of Emergency Radiology, University Hospital Careggi, Largo Brambilla 3, 50134 Florence, Italy
| | - Maria Chiara Brunese
- Diagnostic Imaging Section, Department of Medical and Surgical Sciences & Neurosciences, University of Molise, 86100 Campobasso, Italy
| | - Andrea Belli
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Renato Patrone
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Raffaele Palaia
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Antonio Avallone
- Division of Clinical Experimental Oncology Abdomen, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy
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15
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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16
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Li Z, Huang H, Wang C, Zhao Z, Ma W, Wang D, Mao H, Liu F, Yang Y, Pan W, Lu Z. DCE-MRI radiomics models predicting the expression of radioresistant-related factors of LRP-1 and survivin in locally advanced rectal cancer. Front Oncol 2022; 12:881341. [PMID: 36106114 PMCID: PMC9465298 DOI: 10.3389/fonc.2022.881341] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 08/01/2022] [Indexed: 11/16/2022] Open
Abstract
Objective Low-density lipoprotein receptor-related protein-1 (LRP-1) and survivin are associated with radiotherapy resistance in patients with locally advanced rectal cancer (LARC). This study aimed to evaluate the value of a radiomics model based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for the preoperative assessment of LRP-1 and survivin expressions in these patients. Methods One hundred patients with pathologically confirmed LARC who underwent DCE-MRI before surgery between February 2017 and September 2021 were included in this retrospective study. DCE-MRI perfusion histogram parameters were calculated for the entire lesion using post-processing software (Omni Kinetics, G.E. Healthcare, China), with three quantitative parameter maps. LRP-1 and survivin expressions were assessed by immunohistochemical methods and patients were classified into low- and high-expression groups. Results Four radiomics features were selected to construct the LRP-1 discrimination model. The LRP-1 predictive model achieved excellent diagnostic performance, with areas under the receiver operating curve (AUCs) of 0.853 and 0.747 in the training and validation cohorts, respectively. The other four radiomics characteristics were screened to construct the survivin predictive model, with AUCs of 0.780 and 0.800 in the training and validation cohorts, respectively. Decision curve analysis confirmed the clinical usefulness of the radiomics models. Conclusion DCE-MRI radiomics models are particularly useful for evaluating LRP-1 and survivin expressions in patients with LARC. Our model has significant potential for the preoperative identification of patients with radiotherapy resistance and can serve as an essential reference for treatment planning.
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Affiliation(s)
- Zhiheng Li
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Huizhen Huang
- Shaoxing University School of Medicine, Shaoxing, China
| | - Chuchu Wang
- Shaoxing University School of Medicine, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Weili Ma
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Haijia Mao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Fang Liu
- Department of Pathology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Ye Yang
- Department of Pathology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Weihuo Pan
- Department of Colon and Rectal Surgery, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing Hospital, Zhejiang University School of Medicine, Shaoxing, China
- *Correspondence: Zengxin Lu,
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17
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Tang ZP, Ma Z, He Y, Liu RC, Jin BB, Wen DY, Wen R, Yin HH, Qiu CC, Gao RZ, Ma Y, Yang H. Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery. BMC Med Imaging 2022; 22:147. [PMID: 35996097 PMCID: PMC9396799 DOI: 10.1186/s12880-022-00879-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/16/2022] [Indexed: 12/21/2022] Open
Abstract
Objective To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. Methods A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. Results The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. Conclusion The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.
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Affiliation(s)
- Zhi-Ping Tang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Zhen Ma
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.,Department of Medical Ultrasound, Guangxi International Zhuang Medical Hospital, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Ruo-Chuan Liu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Bin-Bin Jin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Dong-Yue Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rong Wen
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Hai-Hui Yin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Cheng-Cheng Qiu
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China
| | - Yan Ma
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, China.
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18
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med 2022; 127:763-772. [PMID: 35653011 DOI: 10.1007/s11547-022-01501-9] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 04/27/2022] [Indexed: 12/11/2022]
Abstract
PURPOSE The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures. RESULTS The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model. CONCLUSIONS Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Fisciano, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Federica Dell'Aversana
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Lucrezia Silvestro
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Antonio Avallone
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale, Naples, Italy
| | - Federica Flammia
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134, Florence, Italy
| | - Fabiana Tatangelo
- Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, 80131, Naples, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS Di Napoli, Naples, Italy
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19
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Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern. Diagnostics (Basel) 2022; 12:diagnostics12051115. [PMID: 35626271 PMCID: PMC9140199 DOI: 10.3390/diagnostics12051115] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 04/11/2022] [Accepted: 04/27/2022] [Indexed: 02/07/2023] Open
Abstract
To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.
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20
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Granata V, Fusco R, Belli A, Borzillo V, Palumbo P, Bruno F, Grassi R, Ottaiano A, Nasti G, Pilone V, Petrillo A, Izzo F. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect Agent Cancer 2022; 17:13. [PMID: 35346300 PMCID: PMC8961950 DOI: 10.1186/s13027-022-00429-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/18/2022] [Indexed: 02/08/2023] Open
Abstract
Background This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC). Methods Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.
Results We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs. Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance. Conclusions Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.
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21
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Fusco R, Granata V, Grazzini G, Pradella S, Borgheresi A, Bruno A, Palumbo P, Bruno F, Grassi R, Giovagnoni A, Grassi R, Miele V, Barile A. Radiomics in medical imaging: pitfalls and challenges in clinical management. Jpn J Radiol 2022; 40:919-929. [PMID: 35344132 DOI: 10.1007/s11604-022-01271-4] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/14/2022] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye. METHODS This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed. RESULTS Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting. CONCLUSIONS Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.
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Affiliation(s)
| | - Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy.
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Silvia Pradella
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Alessandra Borgheresi
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Alessandra Bruno
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, Abruzzo Health Unit 1, 67100, L'Aquila, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Giovagnoni
- Department of Clinical Special and Dental Sciences, School of Radiology, University Politecnica delle Marche, Ancona, Italy
| | - Roberto Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Division of Radiology, "Università Degli Studi della Campania Luigi Vanvitelli", Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Antonio Barile
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy.,Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, 67100, L'Aquila, Italy
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22
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Grassi R, Grassi F, Ottaiano A, Nasti G, Tatangelo F, Pilone V, Miele V, Brunese MC, Izzo F, Petrillo A. Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases. Radiol Med 2022; 127:461-470. [PMID: 35347583 DOI: 10.1007/s11547-022-01477-6] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 02/25/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients. METHODS This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered. RESULTS The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%. CONCLUSIONS Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100, Campobasso, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Roberta Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, Naples, Italy
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Fabiana Tatangelo
- Division of Pathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Vincenzo Pilone
- Department of Medicine, Surgery and Dentistry, University of Salerno, Salerno, Italy
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, via della Signora 2, 20122, Milan, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences "V. Tiberio", University of Molise, 86100, Campobasso, Italy
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022; 17:6. [PMID: 35246207 PMCID: PMC8897888 DOI: 10.1186/s13027-022-00422-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 02/28/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting. METHODS This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed. RESULTS In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring. CONCLUSIONS Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model. TRIAL REGISTRATION Not applicable.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy.
| | | | - Sergio Venazio Setola
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Igino Simonetti
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
| | - Diletta Cozzi
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Giulia Grazzini
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesca Grassi
- Division of Radiology, "Università Degli Studi Della Campania Luigi Vanvitelli", Naples, Italy
| | - Andrea Belli
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Vittorio Miele
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Florence, Italy.,Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via Della Signora 2, 20122, Milan, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli", Via Mariano Semmola 80131, Naples, Italy
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Liu ET, Zhou S, Li Y, Zhang S, Ma Z, Guo J, Guo L, Zhang Y, Guo Q, Xu L. Development and validation of an MRI-based nomogram for the preoperative prediction of tumor mutational burden in lower-grade gliomas. Quant Imaging Med Surg 2022; 12:1684-1697. [PMID: 35284257 PMCID: PMC8899970 DOI: 10.21037/qims-21-300] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 08/30/2021] [Indexed: 09/25/2023]
Abstract
BACKGROUND High tumor mutational burden (TMB) is an emerging biomarker of sensitivity to immune checkpoint inhibitors. In this study, we aimed to determine the value of magnetic resonance (MR)-based preoperative nomogram in predicting TMB status in lower-grade glioma (LGG) patients. METHODS Overall survival (OS) data were derived from The Cancer Genome Atlas (TCGA) and then analyzed by using the Kaplan-Meier method and time-dependent receiver operating characteristic (tdROC) analysis. The magnetic resonance imaging (MRI) data of 168 subjects obtained from The Cancer Imaging Archive (TCIA) were retrospectively analyzed. The correlation was explored by univariate and multivariate regression analyses. Finally, we performed tenfold cross validation. TMB values were retrieved from the supplementary information of a previously published article. RESULTS The high TMB subtype was associated with the shortest median OS (high vs. low: 50.9 vs. 95.6 months, P<0.05). The tdROC for the high-TMB tumors was 74% (95% CI: 61-86%) for survival at 12 months, and 71% (95% CI: 60-82%) for survival at 24 months. Multivariate logistic regression analysis confirmed that three risk factors [extranodular growth: odds ratio (OR): 8.367, 95% CI: 3.153-22.199, P<0.01; length-width ratio ≥ median: OR: 1.947, 95% CI: 1.025-3.697, P<0.05; frontal lobe: OR: 0.455, 95% CI: 0.229-0.903, P<0.05] were significant independent predictors of high-TMB tumors. The nomogram showed good calibration and discrimination. This model had an area under the curve (AUC) of 0.736 (95% CI: 0.655-0.817). Decision curve analysis (DCA) demonstrated that the nomogram was clinically useful. The average accuracy of the tenfold cross validation was 71.6% for high-TMB tumors. CONCLUSIONS Our results indicated that a distinct OS disadvantage was associated with the high TMB group. In addition, extranodular growth, nonfrontal lobe tumors and length-width ratio ≥ median can be conveniently used to facilitate the prediction of high-TMB tumors.
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Affiliation(s)
- En-Tao Liu
- WeiLun PET Center, Department of Nuclear Medicine, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Shuqin Zhou
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yingwen Li
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Siwei Zhang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Zelan Ma
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Junbiao Guo
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Lei Guo
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Yue Zhang
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Quanlai Guo
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Li Xu
- The Second Affiliated Hospital of Guangzhou University of Chinese Medicine and Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
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EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14051239. [PMID: 35267544 PMCID: PMC8909637 DOI: 10.3390/cancers14051239] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Revised: 02/24/2022] [Accepted: 02/25/2022] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Abstract The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.
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Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, dell’ Aversana F, Ottaiano A, Avallone A, Nasti G, Grassi F, Pilone V, Miele V, Brunese L, Izzo F, Petrillo A. Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study. Cancers (Basel) 2022; 14:cancers14051110. [PMID: 35267418 PMCID: PMC8909569 DOI: 10.3390/cancers14051110] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity. Abstract Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
- Correspondence:
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy; (F.D.M.); (L.B.)
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy; (C.C.); (V.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
| | - Federica dell’ Aversana
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (F.d.A.); (F.G.)
| | - Alessandro Ottaiano
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Antonio Avallone
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Guglielmo Nasti
- Division of Abdominal Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (A.O.); (A.A.); (G.N.)
| | - Francesca Grassi
- Division of Radiology, Università Degli Studi Della Campania Luigi Vanvitelli, 80138 Naples, Italy; (F.d.A.); (F.G.)
| | - Vincenzo Pilone
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084 Salerno, Italy; (C.C.); (V.P.)
| | - Vittorio Miele
- Division of Radiology, Azienda Ospedaliera Universitaria Careggi, 50134 Florence, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, Via della Signora 2, 20122 Milan, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100 Campobasso, Italy; (F.D.M.); (L.B.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale–IRCCS di Napoli, 80131 Naples, Italy; (S.V.S.); (A.P.)
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Perisetti A, Goyal H, Yendala R, Chandan S, Tharian B, Thandassery RB. Sarcopenia in hepatocellular carcinoma: Current knowledge and future directions. World J Gastroenterol 2022; 28:432-448. [PMID: 35125828 PMCID: PMC8790553 DOI: 10.3748/wjg.v28.i4.432] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 06/29/2021] [Accepted: 01/11/2022] [Indexed: 02/06/2023] Open
Abstract
Liver cancer is the second most occurring cancer worldwide and is one of the leading causes of cancer-related deaths. Hepatocellular carcinoma (HCC) is the most common (80%-90%) type among malignant liver cancers. Sarcopenia occurs very early in HCC and can predict and provide an opportunity to improve muscle health before engaging in the treatment options such as loco-regional, systemic, and transplant management. Multiple prognostic stating systems have been developed in HCC, such as Barcelona Clinic Liver Cancer, Child-Pugh score and Albumin-Bilirubin grade. However, the evaluation of patients' performance status is a major limitation of these scoring systems. In this review, we aim to summarize the current knowledge and recent advances about the role of sarcopenia in cirrhosis in general, while focusing specifically on HCC. Additionally, the role of sarcopenia in predicting clinical outcomes and prognostication in HCC patients undergoing loco-regional therapies, liver resection, liver transplantation and systematic therapy has been discussed. A literature review was performed using databases PubMed/MEDLINE, EMBASE, Cochrane, Web of Science, and CINAHL on April 1, 2021, to identify published reports on sarcopenia in HCC. Sarcopenia can independently predict HCC-related mortality especially in patients undergoing treatments such as loco-regional, surgical liver transplantation and systemic therapies. Basic research is focused on evaluating a balance of anabolic and catabolic pathways responsible for muscle health. Early clinical studies have shown promising results in methods to improve sarcopenia in HCC which can potentially increase prognosis in these patients. As sarcopenia occurs very early in HCC, it can predict and provide an opportunity to improve muscle health before engaging in the treatment options such as loco-regional, systemic, and transplant management. Further, sarcopenia measurement can obviate the confounding caused by the abdominal ascites in these patients. The use of sarcopenia can add to the existing scoring systems to better prognosticate the HCC.
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Affiliation(s)
- Abhilash Perisetti
- Department of Internal Medicine, Gastroenterology and Hepatology Division, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
- Department of Interventional Oncology and Surgical Endoscopy, Parkview Health, Fort Wayne, IN 46825, United States
| | - Hemant Goyal
- Department of Internal Medicine, The Wright Center for Graduate Medical Education, The Wright Center for Graduate Medical Education, Scranton, PA 18501, United States
| | - Rachana Yendala
- Department of Hematology and Oncology, Conway Regional Medical Center, Conway, AR 72034, United States
| | - Saurabh Chandan
- Department of Internal Medicine, Gastroenterology and Hepatology Division, CHI Creighton University Medical Center, Omaha, NE 68107, United States
| | - Benjamin Tharian
- Department of Internal Medicine, Gastroenterology and Hepatology Division, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States
| | - Ragesh Babu Thandassery
- Department of Medicine, Central Arkansas Veterans Healthcare System, Little Rock, AR 72205, United States
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Tang YY, Zhao YN, Zhang T, Chen ZY, Ma XL. Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma. World J Gastroenterol 2021; 27:7173-7189. [PMID: 34887636 PMCID: PMC8613648 DOI: 10.3748/wjg.v27.i41.7173] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/26/2021] [Accepted: 09/03/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Combined hepatocellular carcinoma (HCC) and cholangiocarcinoma (cHCC-CCA) is defined as a single nodule showing differentiation into HCC and intrahepatic cholangiocarcinoma and has a poor prognosis. AIM To develop a radiomics nomogram for predicting post-resection survival of patients with cHCC-CCA. METHODS Patients with pathologically diagnosed cHCC-CCA were randomly divided into training and validation sets. Radiomics features were extracted from portal venous phase computed tomography (CT) images using the least absolute shrinkage and selection operator Cox regression and random forest analysis. A nomogram integrating the radiomics score and clinical factors was developed using univariate analysis and multivariate Cox regression. Nomogram performance was assessed in terms of the C-index as well as calibration, decision, and survival curves. RESULTS CT and clinical data of 118 patients were included in the study. The radiomics score, vascular invasion, anatomical resection, total bilirubin level, and satellite lesions were found to be independent predictors of overall survival (OS) and were therefore included in an integrative nomogram. The nomogram was more strongly associated with OS (hazard ratio: 8.155, 95% confidence interval: 4.498-14.785, P < 0.001) than a model based on the radiomics score or only clinical factors. The area under the curve values for 1-year and 3-year OS in the training set were 0.878 and 0.875, respectively. Patients stratified as being at high risk of poor prognosis showed a significantly shorter median OS than those stratified as being at low risk (6.1 vs 81.6 mo, P < 0.001). CONCLUSION This nomogram may predict survival of cHCC-CCA patients after hepatectomy and therefore help identify those more likely to benefit from surgery.
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Affiliation(s)
- You-Yin Tang
- Department of Liver Surgery, Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yu-Nuo Zhao
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, West China Hospital, Chengdu 610041, Sichuan Province, China
| | - Tao Zhang
- West China School of Medicine of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zhe-Yu Chen
- Department of Liver Surgery, Liver Transplantation Center, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Xue-Lei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, West China Hospital, Chengdu 610041, Sichuan Province, China
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Ren S, Li Q, Liu S, Qi Q, Duan S, Mao B, Li X, Wu Y, Zhang L. Clinical Value of Machine Learning-Based Ultrasomics in Preoperative Differentiation Between Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma: A Multicenter Study. Front Oncol 2021; 11:749137. [PMID: 34804935 PMCID: PMC8604281 DOI: 10.3389/fonc.2021.749137] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE This study aims to explore the clinical value of machine learning-based ultrasomics in the preoperative noninvasive differentiation between hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). METHODS The clinical data and ultrasonic images of 226 patients from three hospitals were retrospectively collected and divided into training set (n = 149), test set (n = 38), and independent validation set (n = 39). Manual segmentation of tumor lesion was performed with ITK-SNAP, the ultrasomics features were extracted by the pyradiomics, and ultrasomics signatures were generated using variance filtering and lasso regression. The prediction models for preoperative differentiation between HCC and ICC were established by using support vector machine (SVM). The performance of the three models was evaluated by the area under curve (AUC), sensitivity, specificity, and accuracy. RESULTS The ultrasomics signatures extracted from the grayscale ultrasound images could successfully differentiate between HCC and ICC (p < 0.05). The combined model had a better performance than either the clinical model or the ultrasomics model. In addition to stability, the combined model also had a stronger generalization ability (p < 0.05). The AUC (along with 95% CI), sensitivity, specificity, and accuracy of the combined model on the test set and the independent validation set were 0.936 (0.806-0.989), 0.900, 0.857, 0.868, and 0.874 (0.733-0.961), 0.889, 0.867, and 0.872, respectively. CONCLUSION The ultrasomics signatures could facilitate the preoperative noninvasive differentiation between HCC and ICC. The combined model integrating ultrasomics signatures and clinical features had a higher clinical value and a stronger generalization ability.
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Affiliation(s)
- Shanshan Ren
- Henan University People’s Hospital, Zhengzhou, China
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qian Li
- Henan Provincial Cancer Hospital, Zhengzhou, China
| | - Shunhua Liu
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Qinghua Qi
- First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Shaobo Duan
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Bing Mao
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Xin Li
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Yuejin Wu
- Henan Provincial People’s Hospital, Zhengzhou, China
| | - Lianzhong Zhang
- Henan University People’s Hospital, Zhengzhou, China
- Henan Provincial People’s Hospital, Zhengzhou, China
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de la Pinta C, Castillo ME, Collado M, Galindo-Pumariño C, Peña C. Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients. Cancers (Basel) 2021; 13:5547. [PMID: 34771709 PMCID: PMC8582778 DOI: 10.3390/cancers13215547] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 11/02/2021] [Accepted: 11/03/2021] [Indexed: 02/07/2023] Open
Abstract
Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data that can be mined for new biomarkers that show the biology of pathological processes at microscopic levels. These data can be converted into image-based signatures to improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination of radiomics and molecular data, called radiogenomics, has clear implications for cancer patients' management. Though some studies have focused on radiogenomics signatures in hepatocellular carcinoma patients, only a few have examined colorectal cancer metastatic lesions in the liver. Moreover, the need to differentiate between liver lesions is fundamental for accurate diagnosis and treatment. In this review, we summarize the knowledge gained from radiomics and radiogenomics studies in hepatic metastatic colorectal cancer patients and their use in early diagnosis, response assessment and treatment decisions. We also investigate their value as possible prognostic biomarkers. In addition, the great potential of image mining to provide a comprehensive view of liver niche formation is examined thoroughly. Finally, new challenges and current limitations for the early detection of the liver premetastatic niche, based on radiomics and radiogenomics, are also discussed.
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Affiliation(s)
- Carolina de la Pinta
- Radiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain
| | - María E. Castillo
- Medical Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain; (M.E.C.); (M.C.); (C.G.-P.)
- Centro de Investigación Biomédica en Red de Cancer (CIBERONC), 28029 Madrid, Spain
| | - Manuel Collado
- Medical Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain; (M.E.C.); (M.C.); (C.G.-P.)
| | - Cristina Galindo-Pumariño
- Medical Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain; (M.E.C.); (M.C.); (C.G.-P.)
- Centro de Investigación Biomédica en Red de Cancer (CIBERONC), 28029 Madrid, Spain
| | - Cristina Peña
- Medical Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain; (M.E.C.); (M.C.); (C.G.-P.)
- Centro de Investigación Biomédica en Red de Cancer (CIBERONC), 28029 Madrid, Spain
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Li Q, Che F, Wei Y, Jiang HY, Zhang Y, Song B. Role of noninvasive imaging in the evaluation of intrahepatic cholangiocarcinoma: from diagnosis and prognosis to treatment response. Expert Rev Gastroenterol Hepatol 2021; 15:1267-1279. [PMID: 34452581 DOI: 10.1080/17474124.2021.1974294] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 08/26/2021] [Indexed: 02/08/2023]
Abstract
INTRODUCTION Intrahepatic cholangiocarcinoma is the second most common liver cancer. Desmoplastic stroma may be revealed as distinctive histopathologic findings favoring intrahepatic cholangiocarcinoma. Meanwhile, a range of imaging manifestations is often accompanied with rich desmoplastic stroma in intrahepatic cholangiocarcinoma, which can indicate large bile duct ICC, and a higher level of cancer-associated fibroblasts with poor prognosis and weak treatment response. AREAS COVERED We provide a comprehensive review of current state-of-the-art and recent advances in the imaging evaluation for diagnosis, staging, prognosis and treatment response of intrahepatic cholangiocarcinoma. In addition, we discuss precursor lesions, cells of origin, molecular mutation, which would cause the different histological classification. Moreover, histological classification and tumor microenvironment, which are related to the proportion of desmoplastic stroma with many imaging manifestations, would be also discussed. EXPERT OPINION The diagnosis, prognosis, treatment response of intrahepatic cholangiocarcinoma may be revealed as the presence and the proportion of desmoplastic stroma with a range of imaging manifestations. With the utility of radiomics and artificial intelligence, imaging is helpful for ICC evaluation. Multicentre, large-scale, prospective studies with external validation are in need to develop comprehensive prediction models based on clinical data, imaging findings, genetic parameters, molecular, metabolic, and immune biomarkers.
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Affiliation(s)
- Qian Li
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Feng Che
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yi Wei
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Han-Yu Jiang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Yun Zhang
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
| | - Bin Song
- Department of Radiology, Sichuan University West China Hospital, Chengdu, China
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Alfieri S, Romanò R, Bologna M, Calareso G, Corino V, Mirabile A, Ferri A, Bellanti L, Poli T, Marcantoni A, Grosso E, Tarsitano A, Battaglia S, Blengio F, De Martino I, Valerini S, Vecchio S, Richetti A, Deantonio L, Martucci F, Grammatica A, Ravanelli M, Ibrahim T, Caruso D, Locati LD, Orlandi E, Bossi P, Mainardi L, Licitra LF. Prognostic role of pre-treatment magnetic resonance imaging (MRI)-based radiomic analysis in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. Acta Oncol 2021; 60:1192-1200. [PMID: 34038324 DOI: 10.1080/0284186x.2021.1924401] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
OBJECTIVES To identify and validate baseline magnetic resonance imaging (b-MRI) radiomic features (RFs) as predictors of disease outcomes in effectively cured head and neck squamous cell carcinoma (HNSCC) patients. MATERIALS AND METHODS Training set (TS) and validation set (VS) were retrieved from preexisting datasets (HETeCo and BD2Decide trials, respectively). Only patients with both pre- and post-contrast enhancement T1 and T2-weighted b-MRI and at least 2 years of follow-up (FUP) were selected. The combination of the best extracted RFs was used to classify low risk (LR) vs. high risk (HR) of disease recurrence. Sensitivity, specificity, and area under the curve (AUC) of the radiomic model were computed on both TS and VS. Overall survival (OS) and 5-year disease-free survival (DFS) Kaplan-Meier (KM) curves were compared for LR vs. HR. The radiomic-based risk class was used in a multivariate Cox model, including well-established clinical prognostic factors (TNM, sub-site, human papillomavirus [HPV]). RESULTS In total, 57 patients of TS and 137 of VS were included. Three RFs were selected for the signature. Sensitivity of recurrence risk classifier was 0.82 and 0.77, specificity 0.78 and 0.81, AUC 0.83 and 0.78 for TS and VS, respectively. VS KM curves for LR vs. HR groups significantly differed both for 5-year DFS (p<.0001) and OS (p=.0004). A combined model of RFs plus TNM improved prognostic performance as compared to TNM alone, both for VS 5-year DFS (C-index: 0.76 vs. 0.60) and OS (C-index: 0.74 vs. 0.64). CONCLUSIONS Radiomics of b-MRI can help to predict recurrence and survival outcomes in HNSCC.
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Affiliation(s)
- Salvatore Alfieri
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Rebecca Romanò
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Marco Bologna
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Giuseppina Calareso
- Radiology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Valentina Corino
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Aurora Mirabile
- Department of Oncology, Division of Experimental Medicine, IRCCS San Raffaele Hospital, Milan, Italy
| | - Andrea Ferri
- Department of Surgery, Maxillo-Facial Surgery Division, University Hospital of Parma, Parma, Italy
| | - Luca Bellanti
- Department of Surgery, Maxillo-Facial Surgery Division, University Hospital of Parma, Parma, Italy
| | - Tito Poli
- Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), Unit of Maxillo-Facial Surgery, University of Parma, Parma, Italy
| | | | - Enrica Grosso
- Division of Head and Neck Surgery, Istituto Europeo di Oncologia (IEO), Milan, Italy
| | - Achille Tarsitano
- Department of Biomedical and Neuromotor Sciences, Maxillofacial Surgery Unit, S. Orsola-Malpighi Hospital, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Salvatore Battaglia
- Department of Biomedical and Neuromotor Sciences, Maxillofacial Surgery Unit, S. Orsola-Malpighi Hospital, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Fulvia Blengio
- Medical Oncology Department, AO SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Iolanda De Martino
- Medical Oncology Department, AO SS Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Sara Valerini
- Neuroscience Head and Neck Department, Otolaryngology Unit, Azienda Ospedaliero Universitaria di Modena, Modena, Italy
| | - Stefania Vecchio
- Medical Oncology 2, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Antonella Richetti
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Letizia Deantonio
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Francesco Martucci
- Radiation Oncology Clinic Oncology, Institute of Southern Switzerland (IOSI), Bellinzona-Lugano, Switzerland
| | - Alberto Grammatica
- Department of Medical and Surgical Specialties, Radiologic Sciences, and Public Health, Unit of Otorhinolaryngology-Head and Neck Surgery, ASST Spedali Civili di Brescia, University of Brescia, Brescia, Italy
| | - Marco Ravanelli
- Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, Unit of Radiology, University of Brescia, Brescia, Italy
| | - Toni Ibrahim
- Osteoncology and Rare Tumors Center, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Damiano Caruso
- Department of Surgical and Medical Sciences and Translational Medicine, Sant'Andrea University Hospital, Sapienza University of Rome, Rome, Italy
| | - Laura Deborah Locati
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Ester Orlandi
- Radiotherapy Unit 2, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
| | - Paolo Bossi
- Medical Oncology, Department of Medical and Surgical Specialties, Radiological Sciences and Public, Health University of Brescia, ASST-Spedali Civili, Brescia, Italy
| | - Luca Mainardi
- Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Lisa F. Licitra
- Head and Neck Cancer Medical Oncology 3 Department, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy
- University of Milan, Milan, Italy
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De la Pinta C. Toward Personalized Medicine in Radiotherapy of Hepatocellular Carcinoma: Emerging Radiomic Biomarker Candidates of Response and Toxicity. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2021; 25:537-544. [PMID: 34448625 DOI: 10.1089/omi.2021.0065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Radiology and radiotherapy are currently undergoing radical transformation with use of biomarkers and digital technologies such as artificial intelligence. These current and upcoming changes in radiology speak of an overarching new vision for personalized medicine. This is particularly evident in the case of radiotherapy of cancers, and of liver cancer in particular. The development of modern radiotherapy with stereotactic body radiotherapy allows targeted treatments to be delivered to the tumor site, limiting the dose to surrounding healthy organs, thus becoming a new therapeutic alternative for hepatocellular carcinoma and other liver tumors. However, not all patients have the same response to radiotherapy or display the same side-effect profile. Biomarkers of response and toxicity in liver radiotherapy would facilitate the vision and practice of personalized medicine. This expert review examines the available molecular, radiomic, and radiogenomic biomarker candidates for acute liver toxicity with potential use for prediction of radiotherapy-induced liver toxicity. To this end, I highlight for oncologists and life scientists that radiomics allows diagnostic images to be analyzed using computer algorithms to extract information imperceptible to the human eye and of relevance to forecasting clinical outcomes. This article underscores particularly (1) the microRNA-based biomarker candidates as among the most promising predictors of radiation-induced liver toxicity and (2) the texture features in radiomic analyses for response prediction. Radiotherapy of hepatocellular carcinoma is edging toward personalized medicine with emerging radiomic biomarker candidates. Future large-scale biomarker studies are called for to enable personalized medicine in liver cancers.
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Affiliation(s)
- Carolina De la Pinta
- Radiation Oncology Department, Ramon y Cajal University Hospital, IRYCIS, Madrid, Spain
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Katabathina VS, Marji H, Khanna L, Ramani N, Yedururi S, Dasyam A, Menias CO, Prasad SR. Decoding Genes: Current Update on Radiogenomics of Select Abdominal Malignancies. Radiographics 2021; 40:1600-1626. [PMID: 33001791 DOI: 10.1148/rg.2020200042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Technologic advances in chromosomal analysis and DNA sequencing have enabled genome-wide analysis of cancer cells, yielding considerable data on the genetic basis of malignancies. Evolving knowledge of tumor genetics and oncologic pathways has led to a better understanding of histopathologic features, tumor classification, tumor biologic characteristics, and imaging findings and discovery of targeted therapeutic agents. Radiogenomics is a rapidly evolving field of imaging research aimed at correlating imaging features with gene mutations and gene expression patterns, and it may provide surrogate imaging biomarkers that may supplant genetic tests and be used to predict treatment response and prognosis and guide personalized treatment options. Multidetector CT, multiparametric MRI, and PET with use of multiple radiotracers are some of the imaging techniques commonly used to assess radiogenomic associations. Select abdominal malignancies demonstrate characteristic imaging features that correspond to gene mutations. Recent advances have enabled us to understand the genetics of steatotic and nonsteatotic hepatocellular adenomas, a plethora of morphologic-molecular subtypes of hepatic malignancies, a variety of clear cell and non-clear cell renal cell carcinomas, a myriad of hereditary and sporadic exocrine and neuroendocrine tumors of the pancreas, and the development of targeted therapeutic agents for gastrointestinal stromal tumors based on characteristic KIT gene mutations. Mutations associated with aggressive phenotypes of these malignancies can sometimes be predicted on the basis of their imaging characteristics. Radiologists should be familiar with the genetics and pathogenesis of common cancers that have associated imaging biomarkers, which can help them be integral members of the cancer management team and guide clinicians and pathologists. Online supplemental material is available for this article. ©RSNA, 2020 See discussion on this article by Luna (pp 1627-1630).
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Affiliation(s)
- Venkata S Katabathina
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Haneen Marji
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Lokesh Khanna
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Nisha Ramani
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Sireesha Yedururi
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Anil Dasyam
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Christine O Menias
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
| | - Srinivasa R Prasad
- From the Department of Radiology, University of Texas Health at San Antonio, 7703 Floyd Curl Dr, San Antonio, TX 78229 (V.S.K., H.M., L.K.); Departments of Radiology (S.Y., S.R.P.) and Pathology (N.R.), University of Texas MD Anderson Cancer Center, Houston, Tex; Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, Pa (A.D.); and Department of Radiology, Mayo Clinic, Scottsdale, Ariz (C.O.M.)
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Lorkowski J, Kolaszyńska O, Pokorski M. Artificial Intelligence and Precision Medicine: A Perspective. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1375:1-11. [PMID: 34138457 DOI: 10.1007/5584_2021_652] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.
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Affiliation(s)
- Jacek Lorkowski
- Department of Orthopedics, Traumatology and Sports Medicine, Central Clinical Hospital of the Ministry of Internal Affairs and Administration, Warsaw, Poland. .,Faculty of Health Sciences, Medical University of Mazovia, Warsaw, Poland.
| | - Oliwia Kolaszyńska
- Department of Cardiology, Independent Public Regional Hospital, Szczecin, Poland
| | - Mieczysław Pokorski
- Institute of Health Sciences, Opole University, Opole, Poland.,Faculty of Health Sciences, The Jan Długosz University in Częstochowa, Częstochowa, Poland
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Qin H, Wu YQ, Lin P, Gao RZ, Li X, Wang XR, Chen G, He Y, Yang H. Ultrasound Image-Based Radiomics: An Innovative Method to Identify Primary Tumorous Sources of Liver Metastases. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2021; 40:1229-1244. [PMID: 32951217 DOI: 10.1002/jum.15506] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 08/17/2020] [Accepted: 08/21/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVES To develop radiomic models of B-mode ultrasound (US) signatures for determining the origin of primary tumors in metastatic liver disease. METHODS A total of 254 patients with a diagnosis of metastatic liver disease were included in this retrospective study. The patients were divided into 3 groups depending on the origin of the primary tumor: group 1 (digestive tract versus non-digestive tract tumors), group 2 (breast cancer versus non-breast cancer), and group 3 (lung cancer versus other malignancies). The patients in each group were allocated to a training or testing set (a ratio of 8:2). The region of interest of liver metastasis was determined through manual differentiation of the tumors, and radiomic signatures were acquired from B-mode US images. Optimal features were selected to develop 3 radiomic models using multiple-dimensionality reduction and classifier screening. The area under the curve (AUC) of the receiver operating characteristic curve was applied to assess each model's performance. RESULTS A total of 5936 features were extracted, and 40, 6, and 14 optimal features were sequentially identified for the development of radiomic models for groups 1, 2, and 3, respectively, with training set AUC values of 0.938, 0.974, and 0.768 and testing set AUC values of 0.767, 0.768, and 0.750. The differences in age, sex, and number of liver metastatic lesions varied greatly between the 4 primary tumors (P < .050). CONCLUSIONS B-mode US radiomic models could be effective supplemental means to identify the origin of hepatic metastatic lesions (ie, unknown primary sites).
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Affiliation(s)
- Hui Qin
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yu-Quan Wu
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Rui-Zhi Gao
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Xin-Rong Wang
- Department of Life Sciences, GE Healthcare, Shanghai, China
| | - Gang Chen
- Department of Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yun He
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasonics, First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Magnetic Resonance Features of Lower-grade Gliomas in Prediction of the Reverse Phase Protein A. J Comput Assist Tomogr 2021; 45:300-307. [PMID: 33512852 DOI: 10.1097/rct.0000000000001132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The Cancer Genome Atlas Research Network identified 4 novel protein expression-defined subgroups in patients with lower-grade gliomas (LGGs). The RPPA3 subtype had high levels of Epidermal Growth Factor Receptor and Human epidermal growth factor receptor-2, further increasing the chances for targeted therapy. In this study, we aimed to explore the relationships between magnetic resonance features and reverse phase protein array (RPPA) subtypes (R1-R4). METHODS Survival estimates for the Cancer Genome Atlas cohort were generated using the Kaplan-Meier method and time-dependent receiver operating characteristic curves. A total of 153 patients with LGG with brain magnetic resonance imaging from The Cancer Imaging Archive were retrospectively analyzed. Least absolute shrinkage and selection operator algorithm was used to reduce the feature dimensions of the RPPA3 subtype. RESULTS A total of 51 (33.3%) RPPA1 subtype, 42 (27.4) RPPA2 subtype, 19 (12.4%) RPPA3 subtype, and 38 (24.8%) RPPA4 subtype were identified. On multivariate logistic regression analysis, subventricular zone involvement [odds ratio (OR), 0.370; P = 0.006; 95% confidence interval (CI), 0.181-0.757) was associated with RPPA1 subtype [area under the curve (AUC), 0.598]. Volume of 60 cm3 or greater (OR, 5.174; P < 0.001; 95% CI, 2.182-12.267) was associated with RPPA2 subtype (AUC, 0.684). Proportion contrast-enhanced tumor greater than 5% (OR, 4.722; P = 0.010; 95% CI, 1.456-15.317), extranodular growth (OR, 5.524; P = 0.010; 95% CI, 1.509-20.215), and L/CS ratio equal to or greater than median (OR, 0.132; P = 0.003; 95% CI, 0.035-0.500) were associated with RPPA3 subtype (AUC, 0.825). Proportion contrast-enhanced tumor greater than 5% (OR, 0.206; P = 0.005; 95% CI, 0.068-0.625) was associated with RPPA4 subtype (AUC, 0.638). For the prediction of RPPA3 subtype, the nomogram showed good discrimination, with an AUC of 0.825 (95% CI, 0.711-0.939) and was well calibrated. The RPPA3 subtype was associated with shortest mean overall survival (RPPA3 subtype vs other: 613 vs 873 days; P < 0.05). The time-dependent receiver operating characteristic curves for the RPPA3 subtype was 0.72 (95% CI, 0.60-0.84) for survival at 1 year. Decision curve analysis indicated that prediction for the RPPA3 model was clinically useful. CONCLUSIONS The RPPA3 subtype is an unfavorable prognostic biomarker for overall survival in patients with LGG. Radiogenomics analysis of magnetic resonance features can predict the RPPA subtype preoperatively and may be of clinical value in tailoring the management strategies in patients with LGG.
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Radiomics signature on dynamic contrast-enhanced MR images: a potential imaging biomarker for prediction of microvascular invasion in mass-forming intrahepatic cholangiocarcinoma. Eur Radiol 2021; 31:6846-6855. [PMID: 33638019 DOI: 10.1007/s00330-021-07793-1] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 01/22/2021] [Accepted: 02/15/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To develop a radiomics signature based on dynamic contrast-enhanced (DCE) MR images for preoperative prediction of microvascular invasion (MVI) in patients with mass-forming intrahepatic cholangiocarcinoma (IMCC). METHODS One hundred twenty-six patients with surgically resected single IMCC (34 MVI-positive and 92 MVI-negative) were enrolled and allocated to training and validation cohorts (7:3 ratio). Findings of clinical characteristics and MR features were analyzed. A radiomics signature was built on the basis of reproducible features by using the least absolute shrinkage and selection operator (LASSO) regression algorithm in the training cohort. The prediction performance of radiomics signature was evaluated by receiver operating characteristics curve (ROC) analysis. Internal validation was performed on an independent cohort containing 38 patients. RESULTS Larger tumor size and higher radiomics score were positively correlated with MVI in both training cohort (p < 0.001, < 0.001, respectively) and validation cohort (p = 0.008, 0.001, respectively). The radiomics signature, consisting of seven wavelet features, showed optimal prediction performance in both training (AUC = 0.873) and validation cohorts (AUC = 0.850). CONCLUSION A radiomics signature derived from DCE-MRI of the liver can be a reliable imaging biomarker for predicting MVI of IMCC, which could aid in tailoring treatment strategies. KEY POINTS • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging can be a useful tool to preoperatively predict MVI of IMCC. • Larger tumor size is positively correlated with MVI of IMCC.
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Alwalid O, Long X, Xie M, Yang J, Cen C, Liu H, Han P. CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture. Front Neurol 2021; 12:619864. [PMID: 33692741 PMCID: PMC7937935 DOI: 10.3389/fneur.2021.619864] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 01/18/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. Methods: Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (n = 274) and prospective test (n = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selected. A logistic regression model was constructed using the selected features and evaluated on training and test cohorts. Radiomics score (Rad-score) was calculated for each patient and compared between ruptured and unruptured aneurysms. Results: Nine radiomics features were selected from the CTA images and used to build the logistic regression model. The radiomics model has shown good performance in the classification of the aneurysm rupture on training and test cohorts [area under the receiver operating characteristic curve: 0.92 [95% confidence interval CI: 0.89-0.95] and 0.86 [95% CI: 0.80-0.93], respectively, p < 0.001]. Rad-score showed statistically significant differences between ruptured and unruptured aneurysms (median, 2.50 vs. -1.60 and 2.35 vs. -1.01 on training and test cohorts, respectively, p < 0.001). Conclusion: The results indicated the potential of aneurysm radiomics features for automatic classification of aneurysm rupture on CTA images.
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Affiliation(s)
- Osamah Alwalid
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xi Long
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Mingfei Xie
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Jiehua Yang
- School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, China
| | - Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | | | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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Koh DM, Ba-Ssalamah A, Brancatelli G, Fananapazir G, Fiel MI, Goshima S, Ju SH, Kartalis N, Kudo M, Lee JM, Murakami T, Seidensticker M, Sirlin CB, Tan CH, Wang J, Yoon JH, Zeng M, Zhou J, Taouli B. Consensus report from the 9 th International Forum for Liver Magnetic Resonance Imaging: applications of gadoxetic acid-enhanced imaging. Eur Radiol 2021; 31:5615-5628. [PMID: 33523304 PMCID: PMC8270799 DOI: 10.1007/s00330-020-07637-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 11/17/2020] [Accepted: 12/16/2020] [Indexed: 12/14/2022]
Abstract
Objectives The 9th International Forum for Liver Magnetic Resonance Imaging (MRI) was held in Singapore in September 2019, bringing together radiologists and allied specialists to discuss the latest developments in and formulate consensus statements for liver MRI, including the applications of gadoxetic acid–enhanced imaging. Methods As at previous Liver Forums, the meeting was held over 2 days. Presentations by the faculty on days 1 and 2 and breakout group discussions on day 1 were followed by delegate voting on consensus statements presented on day 2. Presentations and discussions centered on two main meeting themes relating to the use of gadoxetic acid–enhanced MRI in primary liver cancer and metastatic liver disease. Results and conclusions Gadoxetic acid–enhanced MRI offers the ability to monitor response to systemic therapy and to assist in pre-surgical/pre-interventional planning in liver metastases. In hepatocellular carcinoma, gadoxetic acid–enhanced MRI provides precise staging information for accurate treatment decision-making and follow-up post therapy. Gadoxetic acid–enhanced MRI also has potential, currently investigational, indications for the functional assessment of the liver and the biliary system. Additional voting sessions at the Liver Forum debated the role of multidisciplinary care in the management of patients with liver disease, evidence to support the use of abbreviated imaging protocols, and the importance of standardizing nomenclature in international guidelines in order to increase the sharing of scientific data and improve the communication between centers. Key Points • Gadoxetic acid–enhanced MRI is the preferred imaging method for pre-surgical or pre-interventional planning for liver metastases after systemic therapy. • Gadoxetic acid–enhanced MRI provides accurate staging of HCC before and after treatment with locoregional/biologic therapies. • Abbreviated protocols for gadoxetic acid–enhanced MRI offer potential time and cost savings, but more evidence is necessary. The use of gadoxetic acid–enhanced MRI for the assessment of liver and biliary function is under active investigation. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-020-07637-4.
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Affiliation(s)
- Dow-Mu Koh
- Department of Diagnostic Radiology, Royal Marsden Hospital, Sutton, UK.
| | - Ahmed Ba-Ssalamah
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Giuseppe Brancatelli
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BiND), University of Palermo, Palermo, Italy
| | | | - M Isabel Fiel
- Department of Pathology, Molecular and Cell Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Satoshi Goshima
- Department of Diagnostic Radiology & Nuclear Medicine, Hamamatsu University School of Medicine, Hamamatsu, Japan
| | - Sheng-Hong Ju
- Department of Radiology, Zhongda Hospital, Southeast University, Nanjing, People's Republic of China
| | - Nikolaos Kartalis
- Department of Radiology Huddinge, Karolinska University Hospital, Stockholm, Sweden.,Division of Radiology, CLINTEC, Karolinska Institutet, Stockholm, Sweden
| | - Masatoshi Kudo
- Department of Hepatology and Gastroenterology, Kindai University Faculty of Medicine, Osaka, Japan
| | - Jeong Min Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, South Korea
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Max Seidensticker
- Klinik und Poliklinik für Radiologie, Klinikum der Universität München, Munich, Germany
| | - Claude B Sirlin
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Cher Heng Tan
- Department of Diagnostic Radiology, Tan Tock Seng Hospital, Lee Kong Chian School of Medicine, Singapore, Singapore
| | - Jin Wang
- Department of Radiology, Third Affiliated Hospital of Sun Yat Sen University, Guangzhou, People's Republic of China
| | - Jeong Hee Yoon
- Department of Radiology, College of Medicine, Seoul National University, Seoul, South Korea
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Jian Zhou
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Bachir Taouli
- Department of Diagnostic, Molecular, and Interventional Radiology, BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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A Pre-TACE Radiomics Model to Predict HCC Progression and Recurrence in Liver Transplantation. A Pilot Study on a Novel Biomarker. Transplantation 2021; 105:2435-2444. [PMID: 33982917 DOI: 10.1097/tp.0000000000003605] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
BACKGROUND Despite Trans-Arterial Chemo Embolization (TACE) for hepatocellular carcinoma (HCC), a significant number of patients will develop progression on the liver transplant (LT) waiting list or disease recurrence post-LT. We sought to evaluate the feasibility of a pre-TACE radiomic model, an imaging-based tool to predict these adverse outcomes. METHODS We analyzed the pre-TACE computed tomography images of patients waiting for a LT. The primary endpoint was a combined event that included waitlist dropout for tumor progression or tumor recurrence post-LT. The radiomic features were extracted from the largest HCC volume from the arterial and portal venous phase. A third set of features was created, combining the features from these 2 contrast phases. We applied a LASSO feature selection method and a support vector machine classifier. Three prognostic models were built using each feature set. The models' performance was compared using 5-fold cross-validated Area Under the Receiver Operating Characteristic curves (AUC). RESULTS 88 patients were included, of whom 33 experienced the combined event (37.5%). The median time to dropout was 5.6 months (IQR:3.6-9.3), and the median time for post-LT recurrence was 19.2 months (IQR:6.1-34.0). Twenty-four patients (27.3%) dropped out, and 64 (72.7%) patients were transplanted. Of these, 14 (21.9%) had recurrence post-LT. Model performance yielded a mean AUC of 0.70(±0.07), 0.87(±0.06) and 0.81(±0.06) for the arterial, venous and the combined models, respectively. CONCLUSION A pre-TACE radiomics model for HCC patients undergoing LT may be a useful tool for outcome prediction. Further external model validation with a larger sample size is required.
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Defeudis A, De Mattia C, Rizzetto F, Calderoni F, Mazzetti S, Torresin A, Vanzulli A, Regge D, Giannini V. Standardization of CT radiomics features for multi-center analysis: impact of software settings and parameters. Phys Med Biol 2020; 65:195012. [PMID: 32575082 DOI: 10.1088/1361-6560/ab9f61] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The aim of this multicentric study is an inter-center benchmarking, to assess how different set tools applied to the same radiomics workflow affected the radiomics features (RFs) values. This topic is of key importance to start collaboration between different centers and to bring radiomic studies from benchmark to bedside. A per-lesion analysis was performed on 56 metastases (mts) selected from 14 patients. A single radiologist performed the segmentation of all mts, and RFs were extracted from the same segmentation of each mts, using two different software and file formats. Potential sources of discrepancies were evaluated. The intraclass correlation coefficient was used to describe how strongly the same radiomic measurements calculated in the two different centers resemble each other. Moreover, means of the relative changes of each RF were calculated, compared and gradually reduced. We showed that, after matching all formulas, discrepancies in RFs calculation between two centers ranged from 1% to 277%. Therefore, we evaluated other sources of variability using a stepwise approach, which led us to reduce the inter-center discrepancies to 0% for 22/25 RFs and below 2% for 3 RFs out of 25. In this study we demonstrated that different radiomic applications and masks formats might strongly impact the computation of some RFs. Therefore, when dealing with multi-center studies it is mandatory to adopt all strategies that can help in limiting the differences, thus keeping in mind the feasibility of these strategies in large cohort studies.
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Affiliation(s)
- Arianna Defeudis
- Department of Radiology, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy. Department of Surgical Sciences, University of Turin, Turin, Italy
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Peng Y, Lin P, Wu L, Wan D, Zhao Y, Liang L, Ma X, Qin H, Liu Y, Li X, Wang X, He Y, Yang H. Ultrasound-Based Radiomics Analysis for Preoperatively Predicting Different Histopathological Subtypes of Primary Liver Cancer. Front Oncol 2020; 10:1646. [PMID: 33072550 PMCID: PMC7543652 DOI: 10.3389/fonc.2020.01646] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Accepted: 07/27/2020] [Indexed: 12/12/2022] Open
Abstract
Background Preoperative identification of hepatocellular carcinoma (HCC), combined hepatocellular–cholangiocarcinoma (cHCC-ICC), and intrahepatic cholangiocarcinoma (ICC) is essential for treatment decision making. We aimed to use ultrasound-based radiomics analysis to non-invasively distinguish histopathological subtypes of primary liver cancer (PLC) before surgery. Methods We retrospectively analyzed ultrasound images of 668 PLC patients, comprising 531 HCC patients, 48 cHCC-ICC patients, and 89 ICC patients. The boundary of a tumor was manually determined on the largest imaging slice of the ultrasound medicine image by ITK-SNAP software (version 3.8.0), and then, the high-throughput radiomics features were extracted from the obtained region of interest (ROI) of the tumor. The combination of different dimension-reduction technologies and machine learning approaches was used to identify important features and develop the moderate radiomics model. The comprehensive ability of the radiomics model can be evaluated by the area under the receiver operating characteristic curve (AUC). Results After digitally processing tumor ultrasound images, 5,234 high-throughput radiomics features were obtained. We used the Spearman + least absolute shrinkage and selection operator (LASSO) regression method for feature selection and logistics regression for modeling to develop the HCC-vs-non-HCC radiomics model (composed of 16 features). The Spearman + statistical test + random forest methods were used for feature selection, and logistics regression was applied for modeling to develop the ICC-vs-cHCC-ICC radiomics model (composed of 19 features). The overall performance of the radiomics model in identifying different histopathological types of PLC was moderate, with AUC values of 0.854 (training cohort) and 0.775 (test cohort) in the HCC-vs-non-HCC radiomics model and 0.920 (training cohort) and 0.728 (test cohort) in the ICC-vs-cHCC-ICC radiomics model. Conclusion Ultrasound-based radiomics models can help distinguish histopathological subtypes of PLC and provide effective clinical decision making for the accurate diagnosis and treatment of PLC.
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Affiliation(s)
- Yuting Peng
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Peng Lin
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Linyong Wu
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Da Wan
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yujia Zhao
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Li Liang
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaoyu Ma
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hui Qin
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Yichen Liu
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Hong Yang
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
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Zhu HB, Zheng ZY, Zhao H, Zhang J, Zhu H, Li YH, Dong ZY, Xiao LS, Kuang JJ, Zhang XL, Liu L. Radiomics-based nomogram using CT imaging for noninvasive preoperative prediction of early recurrence in patients with hepatocellular carcinoma. ACTA ACUST UNITED AC 2020; 26:411-419. [PMID: 32490826 DOI: 10.5152/dir.2020.19623] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE The aim of this study was to develop and validate a radiomics nomogram based on radiomics features and clinical data for the non-invasive preoperative prediction of early recurrence (≤2 years) in patients with hepatocellular carcinoma (HCC). METHODS We enrolled 262 HCC patients who underwent preoperative contrast-enhanced computed tomography and curative resection (training cohort, n=214; validation cohort, n=48). We applied propensity score matching (PSM) to eliminate redundancy between clinical characteristics and image features, and the least absolute shrinkage and selection operator (LASSO) was used to prevent overfitting. Next, a radiomics signature, clinical nomogram, and combined clinical-radiomics nomogram were built to predict early recurrence, and we compared the performance and generalization of these models. RESULTS The radiomics signature stratified patients into low-risk and high-risk, which show significantly difference in recurrence free survival and overall survival (P ≤ 0.01). Multivariable analysis identified dichotomised radiomics signature, alpha fetoprotein, and tumour number and size as key early recurrence indicators, which were incorporated into clinical and radiomics nomograms. The radiomics nomogram showed the highest area under the receiver operating characteristic curve (AUC), with significantly superior predictive performance over the clinical nomogram in the training cohort (0.800 vs 0.716, respectively; P = 0.001) and the validation cohort (0.785 vs 0.654, respectively; P = 0.039). CONCLUSION The radiomics nomogram is a non-invasive preoperative biomarker for predicting early recurrence in patients with HCC. This model may be of clinical utility for guiding surveillance follow-ups and identifying optimal interventional strategies.
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Affiliation(s)
- Hong-Bo Zhu
- Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China;Department of Oncology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Ze-Yu Zheng
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Heng Zhao
- Department of Radiology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Jing Zhang
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hong Zhu
- Information Management and Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yue-Hua Li
- Department of Oncology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Zhong-Yi Dong
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lu-Shan Xiao
- Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jun-Jie Kuang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Xiao-Li Zhang
- Department of Pathology, the First Affiliated Hospital of University of South China, Hengyang, China
| | - Li Liu
- Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, China
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Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, Haider MA, Jhaveri KS. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol 2020; 31:244-255. [PMID: 32749585 DOI: 10.1007/s00330-020-07119-7] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 07/29/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on MRI and CT radiomics features. METHODS This retrospective study included 85 patients aged 32 to 86 years with 86 histopathology-proven liver cancers: 24 cHCC-CC, 24 CC, and 38 HCC who had MRI and CT between 2004 and 2018. Initial CT reports and morphological evaluation of MRI features were used to assess the performance of radiologists read. Following tumor segmentation, 1419 radiomics features were extracted using PyRadiomics library and reduced to 20 principle components by principal component analysis. Support vector machine classifier was utilized to evaluate MRI and CT radiomics features for the prediction of cHCC-CC vs. non-cHCC-CC and HCC vs. non-HCC. Histopathology was the reference standard for all tumors. RESULTS Radiomics MRI features demonstrated the best performance for differentiation of cHCC-CC from non-cHCC-CC with the highest AUC of 0.77 (SD 0.19) while CT was of limited value. Contrast-enhanced MRI phases and pre-contrast and portal-phase CT showed excellent performance for the differentiation of HCC from non-HCC (AUC of 0.79 (SD 0.07) to 0.81 (SD 0.13) for MRI and AUC of 0.81 (SD 0.06) and 0.71 (SD 0.15) for CT phases, respectively). The misdiagnosis of cHCC-CC as HCC or CC using radiologists read was 69% for CT and 58% for MRI. CONCLUSIONS Our results demonstrate promising predictive performance of MRI and CT radiomics features using machine learning analysis for differentiation of cHCC-CC from HCC and CC with potential implications for treatment decisions. KEY POINTS • Retrospective study demonstrated promising predictive performance of MRI radiomics features in the differentiation of cHCC-CC from HCC and CC and of CT radiomics features in the differentiation of HCC from cHCC-CC and CC. • With future validation, radiomics analysis has the potential to inform current clinical practice for the pre-operative diagnosis of cHCC-CC and to enable optimal treatment decisions regards liver resection and transplantation.
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Affiliation(s)
- Xiaoyang Liu
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada
| | - Farzad Khalvati
- Lunenfeld Tanenbaum Research Institute, University of Toronto, Toronto, Canada
| | - Khashayar Namdar
- Lunenfeld Tanenbaum Research Institute, University of Toronto, Toronto, Canada
| | - Sandra Fischer
- Department of Pathology, University Health Network, University of Toronto, Toronto, Canada
| | - Sara Lewis
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bachir Taouli
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Masoom A Haider
- Department of Medical Imaging, Lunenfeld Tanenbaum Research Institute and Sinai Health System, University Health Network, University of Toronto, Toronto, Canada
| | - Kartik S Jhaveri
- Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto, Canada.
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Ma Y, Ma W, Xu X, Cao F. How Does the Delta-Radiomics Better Differentiate Pre-Invasive GGNs From Invasive GGNs? Front Oncol 2020; 10:1017. [PMID: 32766129 PMCID: PMC7378390 DOI: 10.3389/fonc.2020.01017] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 05/22/2020] [Indexed: 12/19/2022] Open
Abstract
Purpose: This study aimed to explore the role of delta-radiomics in differentiating pre-invasive ground-glass nodules (GGNs) from invasive GGNs, compared with radiomics signature. Materials and Methods: A total of 464 patients including 107 pre-invasive GGNs and 357 invasive GGNs were embraced in radiomics signature analysis. 3D regions of interest (ROIs) were contoured with ITK software. By means of ANOVA/MW, correlation analysis, and LASSO, the optimal radiomic features were selected. The logistic classifier of radiomics signature was constructed and radiomic scores (rad-scores) were calculated. A total of 379 patients including 48 pre-invasive GGNs and 331 invasive GGNs with baseline and follow-up CT examinations before surgeries were enrolled in delta-radiomics analysis. Finally, the logistic classifier of delta-radiomics was constructed. The receiver operating characteristic curves (ROCs) were built to evaluate the validity of classifiers. Results: For radiomics signature analysis, six features were selected from 396 radiomic features. The areas under curve (AUCs) of logistic classifiers were 0.865 (95% CI, 0.823–0.900) in the training set and 0.800 (95% CI, 0.724–0.863) in the testing set. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. As the follow-up interval went on, more and more delta-radiomic features became statistically different. The AUC of the delta-radiomics logistic classifier was 0.901 (95% CI, 0.867–0.928), which was higher than that of the radiomics signature. Conclusion: The radiomics signature contributes to distinguish pre-invasive and invasive GGNs. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. More and more delta-radiomic features appeared to be statistically different as follow-up interval prolonged. Delta-radiomics is superior to radiomics signature in differentiating pre-invasive and invasive GGNs.
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Affiliation(s)
- Yanqing Ma
- Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Weijun Ma
- Shaoxing City Keqiao District Hospital of Traditional Chinese Medicine, Shaoxing, China
| | - Xiren Xu
- Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Fang Cao
- Zhejiang Provincial People's Hospital, Hangzhou, China
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Zhang Q, Lou Y, Bai XL, Liang TB. Intratumoral heterogeneity of hepatocellular carcinoma: From single-cell to population-based studies. World J Gastroenterol 2020; 26:3720-3736. [PMID: 32774053 PMCID: PMC7383842 DOI: 10.3748/wjg.v26.i26.3720] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 06/02/2020] [Accepted: 06/18/2020] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is characterized by high heterogeneity in both intratumoral and interpatient manners. While interpatient heterogeneity is related to personalized therapy, intratumoral heterogeneity (ITH) largely influences the efficacy of therapies in individuals. ITH contributes to tumor growth, metastasis, recurrence, and drug resistance and consequently limits the prognosis of patients with HCC. There is an urgent need to understand the causes, characteristics, and consequences of tumor heterogeneity in HCC for the purposes of guiding clinical practice and improving survival. Here, we summarize the studies and technologies that describe ITH in HCC to gain insight into the origin and evolutionary process of heterogeneity. In parallel, evidence is collected to delineate the dynamic relationship between ITH and the tumor ecosystem. We suggest that conducting comprehensive studies of ITH using single-cell approaches in temporal and spatial dimensions, combined with population-based clinical trials, will help to clarify the clinical implications of ITH, develop novel intervention strategies, and improve patient prognosis.
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Affiliation(s)
- Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
- Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou 310003, Zhejiang Province, China
| | - Yu Lou
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
- Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
| | - Xue-Li Bai
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
- Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou 310003, Zhejiang Province, China
| | - Ting-Bo Liang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, Zhejiang Province, China
- Key Laboratory of Pancreatic Disease of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou 310003, Zhejiang Province, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou 310003, Zhejiang Province, China
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Liang ZN, Yang W. Advances in diagnostic application of ultrasomics in liver lesions. Shijie Huaren Xiaohua Zazhi 2020; 28:460-466. [DOI: 10.11569/wcjd.v28.i12.460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
With the progress of medical technology in recent years, radiomics has been rapidly developed and widely used. Ultrasomics, as a branch of radiomics, is gradually applied to liver cancer, breast cancer, and other fields, and some research results have been acknowledged by clinicians. In the study of liver lesions, ultrasound is a vital diagnostic imaging method, but it also has limitations. For example, its performance is inferior to computed tomography or magnetic resonance imaging with regard to the diagnostic specificity for benignity and malignancy. The introduction and progress of ultrasomics provide new methods and ideas that could improve the ability to identify benignity or malignancy of liver lesions, tumor stage, and prognosis of the disease.
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Affiliation(s)
- Zi-Nan Liang
- Department of Ultrasound, Peking University Cancer Hospital, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing 100142, China
| | - Wei Yang
- Department of Ultrasound, Peking University Cancer Hospital, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing 100142, China
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Peng YT, Zhou CY, Lin P, Wen DY, Wang XD, Zhong XZ, Pan DH, Que Q, Li X, Chen L, He Y, Yang H. Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma. Acad Radiol 2020; 27:785-797. [PMID: 31494003 DOI: 10.1016/j.acra.2019.07.029] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 07/27/2019] [Accepted: 07/29/2019] [Indexed: 02/07/2023]
Abstract
RATIONALE AND OBJECTIVES The purpose of this study was to establish and validate radiomics signatures based on ultrasound (US) medicine images to assess the biological behaviors of intrahepatic cholangiocarcinoma (ICC) in a noninvasive manner. MATERIALS AND METHODS This study consisted of 128 ICC patients. We focused on evaluating six pathological features: microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7. Region of interest (ROI) of ICC was identified by manually plotting the tumor contour on the grayscale US image. We extracted radiomics features from medical US imaging. Then, dimensionality reduction methods and classifiers were used to develop radiomic signatures for evaluating six pathological features in ICC. Finally, independent validation datasets were used to assess the radiomic signatures performance. RESULTS We extracted 1076 quantitative characteristic parameters on the US medicine images. Based on extracted radiomics features, the best performing radiomic signatures for evaluating microvascular invasion features were produced by hypothetical test + support vector machine (SVM), perineural invasion subgroup were least absolute shrinkage and selection operator + principal component analysis + support vector machine, differentiation subgroup were hypothetical test + decision tree, Ki-67 subgroup were hypothetical test + logistic regression, vascular endothelial growth factor subgroup were hypothetical test + Gradient Boosting Decision Tree (GBDT), and cytokeratin 7 subgroup were hypothetical test + bagging, respectively. CONCLUSION Through the high-throughput radiomics analysis based on US medicine images, we proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of ICC noninvasively.
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Affiliation(s)
- Yu-Ting Peng
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Chuan-Yang Zhou
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Peng Lin
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Dong-Yue Wen
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Xiao-Dong Wang
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Xiao-Zhu Zhong
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Deng-Hua Pan
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Qiao Que
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China
| | - Xin Li
- GE Healthcare, Shanghai, China
| | | | - Yun He
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China.
| | - Hong Yang
- Department of Medical Ultrasonics, First Afliated Hospital of Guangxi Medical University, No. 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang, China.
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Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, Keek SA, Sanduleanu S, Primakov SP, Beuque MPL, Marcus D, van der Wiel AMA, Zerka F, Oberije CJG, van Timmeren JE, Woodruff HC, Lambin P. Radiomics: from qualitative to quantitative imaging. Br J Radiol 2020; 93:20190948. [PMID: 32101448 DOI: 10.1259/bjr.20190948] [Citation(s) in RCA: 179] [Impact Index Per Article: 35.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.
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Affiliation(s)
- William Rogers
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Sithin Thulasi Seetha
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Thoracic Oncology, IRCCS Foundation National Cancer Institute, Milan, Italy
| | - Turkey A G Refaee
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Diagnostic Radiology, Faculty of Applied Medical Sciences, Jazan University, Jazan, Saudi Arabia
| | - Relinde I Y Lieverse
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Renée W Y Granzier
- Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Surgery, Maastricht University Medical Centre, Grow-School for Oncology and Developmental Biology, Maastricht, The Netherlands
| | - Abdalla Ibrahim
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Nuclear Medicine and Comprehensive diagnostic center Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany.,Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, Hospital Center Universitaire De Liege, Liege, Belgium
| | - Simon A Keek
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sebastian Sanduleanu
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Sergey P Primakov
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Manon P L Beuque
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Damiënne Marcus
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Alexander M A van der Wiel
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Fadila Zerka
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Cary J G Oberije
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Janita E van Timmeren
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiation Oncology, University Hospital Zürich, Zürich, Switzerland.,University of Zürich, Zürich, Switzerland
| | - Henry C Woodruff
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
| | - Philippe Lambin
- The D-Lab & The M-Lab, Department of Precision Medicine, GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands.,Department of Radiology and Nuclear Imaging, GROW-School for Oncology and Developmental Biology, Maastricht University Medical Centre, Maastricht, The Netherlands
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