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Cannarozzi AL, Biscaglia G, Parente P, Latiano TP, Gentile A, Ciardiello D, Massimino L, Di Brina ALP, Guerra M, Tavano F, Ungaro F, Bossa F, Perri F, Latiano A, Palmieri O. Artificial intelligence and whole slide imaging, a new tool for the microsatellite instability prediction in colorectal cancer: Friend or foe? Crit Rev Oncol Hematol 2025; 210:104694. [PMID: 40064251 DOI: 10.1016/j.critrevonc.2025.104694] [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: 12/20/2024] [Revised: 03/03/2025] [Accepted: 03/05/2025] [Indexed: 03/18/2025] Open
Abstract
Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heterogeneous and the response to therapy varies significantly, limiting personalized treatment options. Certain molecular biomarkers, including microsatellite instability (MSI), are critical in planning personalized treatment, although only a subset of patients may benefit. Currently, the primary methods for assessing MSI status include immunohistochemistry (IHC) for DNA mismatch repair proteins (MMRs), polymerase chain reaction (PCR)-based molecular testing, or next-generation sequencing (NGS). However, these techniques have limitations, are expensive and time-consuming, and often result in inter-method inconsistencies. Deficient mismatch repair (dMMR) or high microsatellite instability (MSI-H) are critical predictive biomarkers of response to immune checkpoint inhibitor (ICI) therapy and MSI testing is recommended to identify patients who may benefit. There is a pressing need for a more robust, reliable, and cost-effective approach that accurately assesses MSI status. Recent advances in computational pathology, in particular the development of technologies that digitally scan whole slide images (WSI) at high resolution, as well as new approaches to artificial intelligence (AI) in medicine, are increasingly gaining ground. This review aims to provide an overview of the latest findings on WSI and advances in AI methods for predicting MSI status, summarize their applications in CRC, and discuss their strengths and limitations in daily clinical practice.
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Affiliation(s)
- Anna Lucia Cannarozzi
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Giuseppe Biscaglia
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Paola Parente
- Pathology Unit, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo 71013, Italy.
| | - Tiziana Pia Latiano
- Oncology Unit, Fondazione Casa Sollievo della Sofferenza IRCCS, San Giovanni Rotondo 71013, Italy.
| | - Annamaria Gentile
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Davide Ciardiello
- Division of Gastrointestinal Medical Oncology and Neuroendocrine Tumors, European Institute of Oncology, IEO, IRCCS, Milan.
| | - Luca Massimino
- Gastroenterology and Digestive Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Anna Laura Pia Di Brina
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Maria Guerra
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Francesca Tavano
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Federica Ungaro
- Gastroenterology and Digestive Endoscopy Department, IRCCS Ospedale San Raffaele, Milan, Italy.
| | - Fabrizio Bossa
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Francesco Perri
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Anna Latiano
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
| | - Orazio Palmieri
- Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
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Capello Ingold G, Martins da Fonseca J, Kolenda Zloić S, Verdan Moreira S, Kago Marole K, Finnegan E, Yoshikawa MH, Daugėlaitė S, Souza E Silva TX, Soato Ratti MA. Preoperative radiomics models using CT and MRI for microsatellite instability in colorectal cancer: a systematic review and meta-analysis. Abdom Radiol (NY) 2025:10.1007/s00261-025-04981-1. [PMID: 40347255 DOI: 10.1007/s00261-025-04981-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2025] [Revised: 04/25/2025] [Accepted: 04/29/2025] [Indexed: 05/12/2025]
Abstract
OBJECTIVE Microsatellite instability (MSI) is a novel predictive biomarker for chemotherapy and immunotherapy response, as well as prognostic indicator in colorectal cancer (CRC). The current standard for MSI identification is polymerase chain reaction (PCR) testing or the immunohistochemical analysis of tumor biopsy samples. However, tumor heterogeneity and procedure complications pose challenges to these techniques. CT and MRI-based radiomics models offer a promising non-invasive approach for this purpose. MATERIALS AND METHODS A systematic search of PubMed, Embase, Cochrane Library and Scopus was conducted to identify studies evaluating the diagnostic performance of CT and MRI-based radiomics models for detecting MSI status in CRC. Pooled area under the curve (AUC), sensitivity, and specificity were calculated in RStudio using a random-effects model. Forest plots and a summary ROC curve were generated. Heterogeneity was assessed using I² statistics and explored through sensitivity analyses, threshold effect assessment, subgroup analyses and meta-regression. RESULTS 17 studies with a total of 6,045 subjects were included in the analysis. All studies extracted radiomic features from CT or MRI images of CRC patients with confirmed MSI status to train machine learning models. The pooled AUC was 0.815 (95% CI: 0.784-0.840) for CT-based studies and 0.900 (95% CI: 0.819-0.943) for MRI-based studies. Significant heterogeneity was identified and addressed through extensive analysis. CONCLUSION Radiomics models represent a novel and promising tool for predicting MSI status in CRC patients. These findings may serve as a foundation for future studies aimed at developing and validating improved models, ultimately enhancing the diagnosis, treatment, and prognosis of colorectal cancer.
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Platt JR, Pennycook S, Muthoo CE, Westwood AC, Frood R, Beggs AD, Scarsbrook A, Seligmann JF, Tolan DJM. Colon cancer biology and treatment in the era of precision oncology: A primer for Radiologists. Eur J Radiol 2025; 185:112000. [PMID: 39978239 DOI: 10.1016/j.ejrad.2025.112000] [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: 12/20/2024] [Revised: 02/07/2025] [Accepted: 02/12/2025] [Indexed: 02/22/2025]
Abstract
In the era of precision oncology, systemic therapies for colon cancer are becoming increasingly biomarker-led, with implications for patients in the neoadjuvant, adjuvant and metastatic settings. As the landscape for colon cancer treatment evolves and becomes more complex, it is important that all members of the multidisciplinary team keep abreast of developments to ensure the most effective care is delivered to patients. As core members of the colorectal multidisciplinary team, Radiologists play a central role throughout the patient journey. This review serves as an educational summary of current and emerging treatment pathways in colon cancer, standards for biomarker testing, mechanisms of action for key drugs, important treatment-related complications, relevant tumour biology that underpins patterns of disease and treatment response, and the specific implications systemic therapies have for cancer imaging and Radiologists. We also highlight the increasing role for radiology in patient stratification and the importance of imaging biomarkers. It is crucial that Radiologists understand the current landscape of colon cancer treatment and emerging strategies on the horizon in clinical trials. Only through engagement across the wider multidisciplinary team will we deliver true personalised medicine for patients with colon cancer.
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Affiliation(s)
- James R Platt
- Division of Oncology, Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK.
| | - Stephanie Pennycook
- Department of Medical Oncology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Chand E Muthoo
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
| | - Alice C Westwood
- Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St. James's, School of Medicine, University of Leeds, Leeds, UK.
| | - Russell Frood
- Leeds Institute of Clinical Trials Research, School of Medicine, University of Leeds, Leeds, UK.
| | - Andrew D Beggs
- Department of Cancer and Genomics, University of Birmingham, Birmingham, UK.
| | - Andrew Scarsbrook
- Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK.
| | - Jenny F Seligmann
- Division of Oncology, Leeds Institute of Medical Research at St James's, School of Medicine, University of Leeds, Leeds, UK.
| | - Damian J M Tolan
- Department of Radiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK.
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Chen W, Zheng K, Yuan W, Jia Z, Wu Y, Duan X, Yang W, Wen Z, Zhong L, Liu X. A CT-based deep learning for segmenting tumors and predicting microsatellite instability in patients with colorectal cancers: a multicenter cohort study. LA RADIOLOGIA MEDICA 2025; 130:214-225. [PMID: 39586941 DOI: 10.1007/s11547-024-01909-5] [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: 05/09/2024] [Accepted: 10/23/2024] [Indexed: 11/27/2024]
Abstract
PURPOSE To develop and validate deep learning (DL) models using preoperative contrast-enhanced CT images for tumor auto-segmentation and microsatellite instability (MSI) prediction in colorectal cancer (CRC). MATERIALS AND METHODS Patients with CRC who underwent surgery or biopsy between January 2018 and April 2023 were retrospectively enrolled. Mismatch repair protein expression was determined via immunohistochemistry or fluorescence multiplex polymerase chain reaction-capillary electrophoresis. Manually delineated tumor contours using arterial and venous phase CT images by three abdominal radiologists are served as ground truth. Tumor auto-segmentation used nnU-Net. MSI prediction employed ViT or convolutional neural networks models, trained and validated with arterial and venous phase images (image model) or combined clinical-pathological factors (combined model). The segmentation model was evaluated using patch coverage ratio, Dice coefficient, recall, precision, and F1-score. The predictive models' efficacy was assessed using areas under the curves and decision curve analysis. RESULTS Overall, 2180 patients (median age: 61 years ± 17 [SD]; 1285 males) were divided into training (n = 1159), validation (n = 289), and independent external test (n = 732) groups. High-level MSI status was present in 435 patients (20%). In the external test set, the segmentation model performed well in the arterial phase, with patch coverage ratio, Dice coefficient, recall, precision, and F1-score values of 0.87, 0.71, 0.72, 0.74, and 0.71, respectively. For MSI prediction, the combined models outperformed the clinical model (AUC = 0.83 and 0.82 vs 0.67, p < 0.001) and two image models (AUC = 0.75 and 0.77, p < 0.001). Decision curve analysis confirmed the higher net benefit of the combined model compared to the other models across probability thresholds ranging from 0.1 to 0.45. CONCLUSION DL enhances tumor segmentation efficiency and, when integrated with contrast-enhanced CT and clinicopathological factors, exhibits good diagnostic performance in predicting MSI in CRC.
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Affiliation(s)
- Weicui Chen
- Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China
| | - Kaiyi Zheng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Wenjing Yuan
- Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China
| | - Ziqi Jia
- Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China
| | - Yuankui Wu
- Department of Medical Imaging, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Xiaohui Duan
- Radiology Department, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, Guangdong, China
| | - Wei Yang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China
| | - Zhibo Wen
- Radiology Department, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, Guangdong, China.
| | - Liming Zhong
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
- Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, Guangdong, China.
| | - Xian Liu
- Radiology Department, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China.
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Bodalal Z, Hong EK, Trebeschi S, Kurilova I, Landolfi F, Bogveradze N, Castagnoli F, Randon G, Snaebjornsson P, Pietrantonio F, Lee JM, Beets G, Beets-Tan R. Non-invasive CT radiomic biomarkers predict microsatellite stability status in colorectal cancer: a multicenter validation study. Eur Radiol Exp 2024; 8:98. [PMID: 39186200 PMCID: PMC11347521 DOI: 10.1186/s41747-024-00484-8] [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: 04/29/2024] [Accepted: 05/30/2024] [Indexed: 08/27/2024] Open
Abstract
BACKGROUND Microsatellite instability (MSI) status is a strong predictor of response to immunotherapy of colorectal cancer. Radiogenomic approaches promise the ability to gain insight into the underlying tumor biology using non-invasive routine clinical images. This study investigates the association between tumor morphology and the status of MSI versus microsatellite stability (MSS), validating a novel radiomic signature on an external multicenter cohort. METHODS Preoperative computed tomography scans with matched MSI status were retrospectively collected for 243 colorectal cancer patients from three hospitals: Seoul National University Hospital (SNUH); Netherlands Cancer Institute (NKI); and Fondazione IRCCS Istituto Nazionale dei Tumori, Milan Italy (INT). Radiologists delineated primary tumors in each scan, from which radiomic features were extracted. Machine learning models trained on SNUH data to identify MSI tumors underwent external validation using NKI and INT images. Performances were compared in terms of area under the receiving operating curve (AUROC). RESULTS We identified a radiomic signature comprising seven radiomic features that were predictive of tumors with MSS or MSI (AUROC 0.69, 95% confidence interval [CI] 0.54-0.84, p = 0.018). Integrating radiomic and clinical data into an algorithm improved predictive performance to an AUROC of 0.78 (95% CI 0.60-0.91, p = 0.002) and enhanced the reliability of the predictions. CONCLUSION Differences in the radiomic morphological phenotype between tumors MSS or MSI could be detected using radiogenomic approaches. Future research involving large-scale multicenter prospective studies that combine various diagnostic data is necessary to refine and validate more robust, potentially tumor-agnostic MSI radiogenomic models. RELEVANCE STATEMENT Noninvasive radiomic signatures derived from computed tomography scans can predict MSI in colorectal cancer, potentially augmenting traditional biopsy-based methods and enhancing personalized treatment strategies. KEY POINTS Noninvasive CT-based radiomics predicted MSI in colorectal cancer, enhancing stratification. A seven-feature radiomic signature differentiated tumors with MSI from those with MSS in multicenter cohorts. Integrating radiomic and clinical data improved the algorithm's predictive performance.
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Affiliation(s)
- Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Seoul National University Hospital, Seoul, South Korea
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Ieva Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Federica Landolfi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Francesca Castagnoli
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology, Royal Marsden Hospital, London, UK
- Division of Radiotherapy and Imaging, The Institute of Cancer Research, London, UK
| | - Giovanni Randon
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
| | - Petur Snaebjornsson
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Filippo Pietrantonio
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
- Oncology and Hemato-oncology Department, University of Milan, Milan, Italy
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Geerard Beets
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW Research Institute for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands.
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.
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Holder AM, Dedeilia A, Sierra-Davidson K, Cohen S, Liu D, Parikh A, Boland GM. Defining clinically useful biomarkers of immune checkpoint inhibitors in solid tumours. Nat Rev Cancer 2024; 24:498-512. [PMID: 38867074 DOI: 10.1038/s41568-024-00705-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/14/2024]
Abstract
Although more than a decade has passed since the approval of immune checkpoint inhibitors (ICIs) for the treatment of melanoma and non-small-cell lung, breast and gastrointestinal cancers, many patients still show limited response. US Food and Drug Administration (FDA)-approved biomarkers include programmed cell death 1 ligand 1 (PDL1) expression, microsatellite status (that is, microsatellite instability-high (MSI-H)) and tumour mutational burden (TMB), but these have limited utility and/or lack standardized testing approaches for pan-cancer applications. Tissue-based analytes (such as tumour gene signatures, tumour antigen presentation or tumour microenvironment profiles) show a correlation with immune response, but equally, these demonstrate limited efficacy, as they represent a single time point and a single spatial assessment. Patient heterogeneity as well as inter- and intra-tumoural differences across different tissue sites and time points represent substantial challenges for static biomarkers. However, dynamic biomarkers such as longitudinal biopsies or novel, less-invasive markers such as blood-based biomarkers, radiomics and the gut microbiome show increasing potential for the dynamic identification of ICI response, and patient-tailored predictors identified through neoadjuvant trials or novel ex vivo tumour models can help to personalize treatment. In this Perspective, we critically assess the multiple new static, dynamic and patient-specific biomarkers, highlight the newest consortia and trial efforts, and provide recommendations for future clinical trials to make meaningful steps forwards in the field.
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Affiliation(s)
- Ashley M Holder
- Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | | | | | - Sonia Cohen
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA
| | - David Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Aparna Parikh
- Cancer Center, Massachusetts General Hospital, Boston, MA, USA
| | - Genevieve M Boland
- Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
- Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
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Chen X, Zhuang Z, Pen L, Xue J, Zhu H, Zhang L, Wang D. Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer. Abdom Radiol (NY) 2024; 49:1363-1375. [PMID: 38305796 DOI: 10.1007/s00261-023-04165-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 02/03/2024]
Abstract
PURPOSE To investigate the value of intratumoral and peritumoral radiomics based on contrast-enhanced computer tomography (CECT) to preoperatively predict microsatellite instability (MSI) status in gastric cancer (GC) patients. METHODS A total of 189 GC patients, including 63 patients with MSI-high (MSI-H) and 126 patients with MSI-low/stable (MSI-L/S), were randomly divided into the training cohort and validation cohort. Intratumoral and 5-mm peritumoral regions' radiomics features were extracted from CECT images. The features were standardized by Z-score, and the Inter- and intraclass correlation coefficient, univariate logistic regression analysis, and least absolute shrinkage and selection operator (LASSO) were applied to select the optimal radiomics features. Radiomics scores (Rad-score) based on intratumoral regions, peritumoral regions, and intratumoral + 5-mm peritumoral regions were calculated by weighting the linear combination of the selected features with their respective coefficients to construct the intratumoral model, peritumoral model, and intratumoral + peritumoral model. Logistic regression was used to establish a combined model by combining clinical characteristics, CT semantic features, and Rad-score of intratumoral and peritumoral regions. RESULTS Eleven radiomics features were selected to establish a radiomics intratumoral + peritumoral model. CT-measured tumor length and tumor location were independent risk factors for MSI status. The established combined model obtained the highest area under the receiver operating characteristic (ROC) curve (AUC) of 0.830 (95% CI, 0.727-0.906) in the validation cohort. The calibration curve and decision curve demonstrated its good model fitness and clinical application value. CONCLUSION The combined model based on intratumoral and peritumoral CECT radiomics features and clinical factors can predict the MSI status of GS with moderate accuracy before surgery, which helps formulate personalized treatment strategies.
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Affiliation(s)
- Xingchi Chen
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Zijian Zhuang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Lin Pen
- School of Medicine, Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Jing Xue
- School of Medicine, Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Haitao Zhu
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China
| | - Lirong Zhang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
- Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China.
| | - Dongqing Wang
- Department of Medical Imaging, The Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu Province, China.
- Institute of Imaging and Artificial Intelligence, Jiangsu University, Zhenjiang, 212000, Jiangsu Province, China.
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Bian X, Sun Q, Wang M, Dong H, Dai X, Zhang L, Fan G, Chen G. Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model. BMC Med Imaging 2024; 24:77. [PMID: 38566000 PMCID: PMC10988858 DOI: 10.1186/s12880-024-01252-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 03/18/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.
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Affiliation(s)
- Xuelian Bian
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Qi Sun
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Mi Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Hanyun Dong
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Xiaoxiao Dai
- Department of Pathlogy, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Liyuan Zhang
- Department of Radiotherapy, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China
| | - Guangqiang Chen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, 215004, Suzhou, Jiangsu, China.
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Ma C, Zhao Y, Song Q, Meng X, Xu Q, Tian S, Chen L, Wang N, Song Q, Lin L, Wang J, Liu A. Multi-parametric MRI-based radiomics for preoperative prediction of multiple biological characteristics in endometrial cancer. Front Oncol 2023; 13:1280022. [PMID: 38188296 PMCID: PMC10768555 DOI: 10.3389/fonc.2023.1280022] [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: 08/19/2023] [Accepted: 11/15/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose To develop and validate multi-parametric MRI (MP-MRI)-based radiomics models for the prediction of biological characteristics in endometrial cancer (EC). Methods A total of 292 patients with EC were divided into LVSI (n = 208), DMI (n = 292), MSI (n = 95), and Her-2 (n = 198) subsets. Total 2316 radiomics features were extracted from MP-MRI (T2WI, DWI, and ADC) images, and clinical factors (age, FIGO stage, differentiation degree, pathological type, menopausal state, and irregular vaginal bleeding) were included. Intra-class correlation coefficient (ICC), spearman's rank correlation test, univariate logistic regression, and least absolute shrinkage and selection operator (LASSO) were used to select radiomics features; univariate and multivariate logistic regression were used to identify clinical independent risk factors. Five classifiers were applied (logistic regression, random forest, decision tree, K-nearest neighbor, and Bayes) to construct radiomics models for predicting biological characteristics. The clinical model was built based on the clinical independent risk factors. The combined model incorporating the radiomics score (radscore) and the clinical independent risk factors was constructed. The model was evaluated by ROC curve, calibration curve (H-L test), and decision curve analysis (DCA). Results In the training cohort, the RF radiomics model performed best among the five classifiers for the three subsets (MSI, LVSI, and DMI) according to AUC values (AUCMSI: 0.844; AUCLVSI: 0.952; AUCDMI: 0.840) except for Her-2 subset (Decision tree: AUC=0.714), and the combined model had higher AUC than the clinical model in each subset (MSI: AUCcombined =0.907, AUCclinical =0.755; LVSI: AUCcombined =0.959, AUCclinical =0.835; DMI: AUCcombined = 0.883, AUCclinical =0.796; Her-2: AUCcombined =0.812, AUCclinical =0.717; all P<0.05). Nevertheless, in the validation cohort, significant differences between the two models (combined vs. clinical model) were found only in the DMI and LVSI subsets (DMI: AUCcombined =0.803, AUCclinical =0.698; LVSI: AUCcombined =0.926, AUCclinical =0.796; all P<0.05). Conclusion The radiomics analysis based on MP-MRI and clinical independent risk factors can potentially predict multiple biological features of EC, including DMI, LVSI, MSI, and Her-2, and provide valuable guidance for clinical decision-making.
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Affiliation(s)
- Changjun Ma
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Ying Zhao
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingling Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Xing Meng
- Dalian Women and Children’s Medical Group, Dalian, China
| | - Qihao Xu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Shifeng Tian
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Lihua Chen
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Nan Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
| | - Liangjie Lin
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Jiazheng Wang
- Clinical & Technical Support, Philips Healthcare, Beijing, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Medical Imaging Articial Intelligence Engineering Technology Research Center, Dalian, China
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10
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Liu J, Sun L, Zhao X, Lu X. Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics. J Cancer Res Ther 2023; 19:1552-1559. [PMID: 38156921 DOI: 10.4103/jcrt.jcrt_2633_22] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/05/2023] [Indexed: 01/03/2024]
Abstract
AIM This study aimed to create and validate a clinic-radiomics nomogram based on computed tomography (CT) imaging for predicting preoperative perineural invasion (PNI) of rectal cancer (RC). MATERIAL AND METHODS This study enrolled 303 patients with RC who were divided into training (n = 242) and test datasets (n = 61) in an 8:2 ratio with all their clinical outcomes. A total of 3,296 radiomic features were extracted from CT images. Five machine learning (ML) models (logistic regression (LR)/K-nearest neighbor (KNN)/multilayer perceptron (MLP)/support vector machine (SVM)/light gradient boosting machine (LightGBM)) were developed using radiomic features derived from the arterial and venous phase images, and the model with the best diagnostic performance was selected. By combining the radiomics and clinical signatures, a fused nomogram model was constructed. RESULTS After using the Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) to remove redundant features, the MLP model proved to be the most efficient among the five ML models. The fusion nomogram based on MLP prediction probability further improves the ability to predict the PNI status. The area under the curve (AUC) of the training and test sets was 0.883 and 0.889, respectively, which were higher than those of the clinical (training set, AUC = 0.710; test set, AUC = 0.762) and radiomic models (training set, AUC = 0.840; test set, AUC = 0.834). CONCLUSIONS The clinical-radiomics combined nomogram model based on enhanced CT images efficiently predicted the PNI status of patients with RC.
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Affiliation(s)
- Jiaxuan Liu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Lingling Sun
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Xiang Zhao
- Institute of Innovative Science and Technology, Shenyang University, Liaoning, China
| | - Xi Lu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
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11
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Xie Z, Zhang Q, Wang X, Chen Y, Deng Y, Lin H, Wu J, Huang X, Xu Z, Chi P. Development and validation of a novel radiomics nomogram for prediction of early recurrence in colorectal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2023; 49:107118. [PMID: 37844471 DOI: 10.1016/j.ejso.2023.107118] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 09/25/2023] [Accepted: 10/10/2023] [Indexed: 10/18/2023]
Abstract
BACKGROUND Early recurrence (ER) is a significant concern following curative resection of advanced colorectal cancer (CRC) and is linked to poor long-term survival. Reliable prediction of ER is challenging, necessitating the development of a novel radiomics-based nomogram for CRC patients. METHODS We enrolled 405 patients, with 298 in the training set and 107 in the external test set. Radiomic features were extracted from preoperative venous-phase computed tomography (CT) images. A radiomics signature was created using univariate logistic regression analyses and the least absolute shrinkage and selection operator algorithm. Clinical factors were integrated into the analyses to develop a comprehensive predictive tool in a multivariate logistic regression model, resulting in a radiomics nomogram. Subsequently, the calibration, discrimination, and clinical usefulness of the nomogram were evaluated. RESULTS The radiomics signature, consisting of four selected CT features, was significantly associated with ER in both the training and test datasets (P < 0.05). Independent predictors of ER included TNM stage, carcinoembryonic antigen level and differentiation grade were identified. The radiomics nomogram, incorporating all these predictors, exhibited good predictive ability in both the training set with an area under the curve (AUC) of 0.82 (95 % confidence interval (CI), 0.74-0.90) and the test set with an AUC of 0.85 (95 % CI, 0.72-0.99), surpassing the performance of any single candidate factor alone. Furthermore, additional analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS We have developed a radiomics-based nomogram that effectively predicts early recurrence in CRC patients, enhancing the potential for timely intervention and improved outcomes.
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Affiliation(s)
- Zhongdong Xie
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Qingwei Zhang
- Division of Gastroenterology and Hepatology, Key Laboratory of Digestive Diseases, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai, China
| | - Xiaojie Wang
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Yongchun Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yu Deng
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China
| | - Hanbin Lin
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jiashu Wu
- Department of Science and Technology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xinming Huang
- Department of Radiology, Union Hospital, Fujian Medical University, Fuzhou, China.
| | - Zongbin Xu
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China.
| | - Pan Chi
- Department of Colorectal Surgery, Union Hospital, Fujian Medical University, Fuzhou, China.
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12
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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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
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, 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, Naples, Italy
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Li M, Xu G, Cui Y, Wang M, Wang H, Xu X, Duan S, Shi J, Feng F. CT-based radiomics nomogram for the preoperative prediction of microsatellite instability and clinical outcomes in colorectal cancer: a multicentre study. Clin Radiol 2023; 78:e741-e751. [PMID: 37487841 DOI: 10.1016/j.crad.2023.06.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 06/15/2023] [Accepted: 06/29/2023] [Indexed: 07/26/2023]
Abstract
AIM To develop and validate a computed tomography (CT)-based radiomics nomogram for preoperative prediction of microsatellite instability (MSI) status and clinical outcomes in colorectal cancer (CRC) patients. MATERIALS AND METHODS This retrospective study enrolled 497 CRC patients from three centres. Least absolute shrinkage and selection operator regression was utilised for feature selection and constructing the radiomics signature. Univariate and multivariate logistic regression analyses were employed to identify significant clinical variables. The radiomics nomogram was constructed by integrating the radiomics signature and the identified clinical variables. The performance of the nomogram was evaluated through receiver operating characteristic curves, calibration curves, and decision curve analysis. Kaplan-Meier analysis was performed to investigate the prognostic value of the nomogram. RESULTS The radiomics signature comprised 10 radiomics features associated with MSI status. The nomogram, integrating the radiomics signature and independent predictors (age, location, and thickness), demonstrated favourable calibration and discrimination, achieving areas under the receiver operating characteristic (ROC) curves (AUCs) of 0.89 (95% confidence interval [CI]: 0.83-0.95), 0.87 (95% CI: 0.79-0.95), 0.88 (95% CI: 0.81-0.96), and 0.86 (95% CI: 0.78-0.93) in the training cohort, internal validation cohort, and two external validation cohorts, respectively. The nomogram exhibited superior performance compared to the clinical model (p<0.05). Additionally, survival analysis demonstrated that the nomogram successfully stratified stage II CRC patients based on prognosis (hazard ratio [HR]: 0.357, p=0.022). CONCLUSION The radiomics nomogram demonstrated promising performance in predicting MSI status and stratifying the prognosis of patients with CRC.
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Affiliation(s)
- M Li
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong 226001, Jiangsu Province, China; Department of Radiology, Yancheng No. 1 People's Hospital, Yancheng 224006, Jiangsu Province, China
| | - G Xu
- Department of Radiology, Yancheng No. 1 People's Hospital, Yancheng 224006, Jiangsu Province, China; Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, China
| | - Y Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi 030013, Shanxi Province, China
| | - M Wang
- Department of Radiology, Yancheng No. 1 People's Hospital, Yancheng 224006, Jiangsu Province, China
| | - H Wang
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - X Xu
- Department of Radiotherapy, Affiliated Tumour Hospital of Nantong University, Nantong 226001, Jiangsu Province, China
| | - S Duan
- GE Healthcare China, Shanghai 210000, China
| | - J Shi
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong 226001, Jiangsu Province, China.
| | - F Feng
- Department of Radiology, Affiliated Tumour Hospital of Nantong University, Nantong 226001, Jiangsu Province, China.
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14
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Wang X, Liu Z, Yin X, Yang C, Zhang J. A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis. BMC Gastroenterol 2023; 23:308. [PMID: 37700238 PMCID: PMC10498531 DOI: 10.1186/s12876-023-02922-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 08/10/2023] [Indexed: 09/14/2023] Open
Abstract
PURPOSE To study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery. METHODS The study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H). RESULTS The model including radiomic features and clinical features in the expanded data worked best in the validation group. CONCLUSION A logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.
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Affiliation(s)
- Xuehu Wang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China.
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China.
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China.
| | - Ziqi Liu
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Xiaoping Yin
- Affiliated Hospital of Hebei University, Bao Ding, 071000, China
| | - Chang Yang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
| | - Jushuo Zhang
- College of Electronic and Information Engineering, Hebei University, Baoding, 071002, China
- Research Center of Machine Vision Engineering & Technology of Hebei Province, Baoding, 071002, China
- Key Laboratory of Digital Medical Engineering of Hebei Province, Baoding, 071002, China
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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Kim S, Lee JH, Park EJ, Lee HS, Baik SH, Jeon TJ, Lee KY, Ryu YH, Kang J. Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics. Yonsei Med J 2023; 64:320-326. [PMID: 37114635 PMCID: PMC10151228 DOI: 10.3349/ymj.2022.0548] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/11/2023] [Accepted: 03/20/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients. MATERIALS AND METHODS Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters. RESULTS The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015). CONCLUSION Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.
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Affiliation(s)
- Soyoung Kim
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jae-Hoon Lee
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
| | - Eun Jung Park
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Korea
| | - Seung Hyuk Baik
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Tae Joo Jeon
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Kang Young Lee
- Department of Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Jeonghyun Kang
- Department of Surgery, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
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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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18
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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19
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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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20
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Zhao H, Gao J, Bai B, Wang R, Yu J, Lu H, Cheng M, Liang P. Development and external validation of a non-invasive imaging biomarker to estimate the microsatellite instability status of gastric cancer and its prognostic value: The combination of clinical and quantitative CT-imaging features. Eur J Radiol 2023; 162:110719. [PMID: 36764010 DOI: 10.1016/j.ejrad.2023.110719] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/08/2023] [Accepted: 01/25/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE Molecular testing for microsatellite instability (MSI) status plays a vital role in the clinical management of gastric cancer (GC). Nevertheless, challenges of routinely applied technology for MSI determination exist. This study aimed to develop and validate a non-invasive imaging biomarker for MSI assessment in GC and explore its prognostic value. METHODS We retrospectively recruited 396 GC patients with pretreatment CT images from a single center and a public database and divided them into an original cohort (n = 356) and an external validation cohort (n = 40). The SMOTE algorithm was used to generate a balanced training cohort (n = 192) and the independent radiomics model, clinical model, and radiomics-clinic combined model were constructed for determining MSI status. The models' discrimination, calibration, clinical usefulness, and prognosis significance were evaluated by AUC, calibration, decision curve analyses, and Kaplan-Meier curve analysis, respectively. RESULTS The radiomics-clinic combined model derived from clinical and quantitative CT-based "Radscore" exhibited the best discriminatory abilities of MSI status in all cohorts, with AUCs of 0.836 (95% CI, 0.780-0.893) in the training cohort, 0.834 (95% CI, 0.688-0.981) in the external validation cohort, and 0.750 (95% CI, 0.682-0.819) in the original cohort, respectively. Meanwhile, the combined model demonstrated goodness of fitness, higher clinical net benefits, and significant positive integrated discrimination improvement compared with any independent model. While it showed no significant overall survival- or progression-free survival-based risk stratification ability (p > 0.05). CONCLUSIONS The radiomics-clinic combined model could be a potential non-invasive biomarker for MSI status in GC, which help clinical decision-making, nevertheless, provided limited prognostic ability.
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Affiliation(s)
- Huiping Zhao
- Department of CT, Shaanxi Provincial People's Hospital, No. 256, Youyi West Road, Xi'an 710068, Shaanxi Province, China.
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Biaosheng Bai
- Department of Radiotherapy, People's Hospital of Bayingolin Mongol Autonomous Prefecture, Korla 841000, Xinjiang, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Juan Yu
- Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, Hubei Province, China
| | - Hao Lu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
| | - Ming Cheng
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China; Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor & Henan International Joint Laboratory of Medical Imaging & Henan Engineering Laboratory of Tumor Imaging & Henan Key Laboratory of CT Imaging & Zhengzhou Key Laboratory of Medical Imaging Technology and Diagnosis, Zhengzhou 450052, Henan Province, China
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21
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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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22
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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23
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Chen X, He L, Li Q, Liu L, Li S, Zhang Y, Liu Z, Huang Y, Mao Y, Chen X. Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm-enhanced artificial neural network-based CT radiomics signature. Eur Radiol 2023; 33:11-22. [PMID: 35771245 DOI: 10.1007/s00330-022-08954-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 05/08/2022] [Accepted: 06/08/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE The stratification of microsatellite instability (MSI) status assists clinicians in making treatment decisions for colorectal cancer (CRC) patients. This study aimed to establish a CT-based radiomics signature to predict MSI status in patients with CRC. METHODS A total of 837 CRC patients who underwent preoperative enhanced CT and had available MSI status data were recruited from two hospitals. Radiomics features were extracted from segmented tumours, and a series of data balancing and feature selection strategies were used to select MSI-related features. Finally, an MSI-related radiomics signature was constructed using a genetic algorithm-enhanced artificial neural network model. Combined and clinical models were constructed using multivariate logistic regression analyses by integrating the clinical factors with or without the signature. A Kaplan-Meier survival analysis was conducted to explore the prognostic information of the signature in patients with CRC. RESULTS Ten features were selected to construct a signature which showed robust performance in both the internal and external validation cohorts, with areas under the curves (AUC) of 0.788 and 0.775, respectively. The performance of the signature was comparable to that of the combined model (AUCs of 0.777 and 0.767, respectively) and it outperformed the clinical model constituting age and tumour location (AUCs of 0.768 and 0.623, respectively). Survival analysis demonstrated that the signature could stratify patients with stage II CRC according to prognosis (HR: 0.402, p = 0.029). CONCLUSIONS This study built a robust radiomics signature for identifying the MSI status of CRC patients, which may assist individualised treatment decisions. KEY POINTS • Our well-designed modelling strategies helped overcome the problem of data imbalance caused by the low incidence of MSI. • Genetic algorithm-enhanced artificial neural network-based CT radiomics signature can effectively distinguish the MSI status of CRC patients. • Kaplan-Meier survival analysis demonstrated that our signature could significantly stratify stage II CRC patients into high- and low-risk groups.
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Affiliation(s)
- Xiaobo Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Lan He
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Qingshu Li
- Department of Pathology, College of Basic Medicine, Chongqing Medical University, Chongqing, 400016, China
| | - Liu Liu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Suyun Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
| | - Yuan Zhang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Yanqi Huang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
| | - Yun Mao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China.
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China.
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Li M, Gu H, Xue T, Peng H, Chen Q, Zhu X, Duan S, Feng F. CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion in colorectal cancer: a multicenter study. Br J Radiol 2023; 96:20220568. [PMID: 36318241 PMCID: PMC10997017 DOI: 10.1259/bjr.20220568] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/20/2022] [Accepted: 10/11/2022] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVE To develop and externally validate a CT-based radiomics nomogram for the pre-operative prediction of lymphovascular invasion (LVI) in patients with colorectal cancer (CRC). METHODS 357 patients derived from 2 centers with pathologically confirmed CRC were included in this retrospective study. Two-dimensional (2D) and three-dimensional (3D) radiomics features were extracted from portal venous phase CT images. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. The radiomics nomogram was developed by integrating the radiomics score (rad-score) and the clinical risk factor. RESULTS The rad-score was significantly higher in the LVI+ group than in the LVI- group (p < 0.05). The area under the curve (AUC), accuracy, sensitivity and specificity of the 3D radiomics model were higher than those of the 2D radiomics model. The AUCs of 3D and 2D radiomics models in the training set were 0.82 (95% CI: 0.75-0.89) and 0.74 (95% CI: 0.66-0.82); in the internal validation set were 0.75 (95% CI: 0.65-0.85) and 0.67 (95% CI: 0.56-0.78); in the external validation set were 0.75 (95% CI: 0.64-0.86) and 0.57 (95% CI: 0.45-0.69); respectively. The AUCs of the nomogram integrating the optimal 3D rad-score and clinical risk factors (CT-reported T stage, CT-reported lymph node status) in the internal set and external validation set were 0.82 (95% CI: 0.73-0.91) and 0.80 (95% CI: 0.68-0.91), respectively. CONCLUSION Both 2D and 3D radiomics models can predict LVI status of CRC. The nomogram combining the optimal 3D rad-score and clinical risk factors further improved predictive performance. ADVANCES IN KNOWLEDGE This is the first study to compare the difference in performance of CT-based 2D and 3D radiomics models for the pre-operative prediction of LVI in CRC. The prediction of the nomogram could be improved by combining the 3D radiomics model with the imaging model, suggesting its potential for clinical application.
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Affiliation(s)
- Manman Li
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hongmei Gu
- Department of Radiology, Affiliated Hospital of Nantong
University, Nantong, PR China
| | - Ting Xue
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Hui Peng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Qiaoling Chen
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | - Xinghua Zhu
- Department of Pathology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
| | | | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong
University, Nantong, PR China
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Lin Z, Wang T, Li H, Xiao M, Ma X, Gu Y, Qiang J. Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer. Quant Imaging Med Surg 2023; 13:108-120. [PMID: 36620141 PMCID: PMC9816750 DOI: 10.21037/qims-22-255] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 09/05/2022] [Indexed: 11/30/2022]
Abstract
Background Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC. Methods A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA). Results Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model. Conclusions The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.
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Affiliation(s)
- Zijing Lin
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ting Wang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Haiming Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Meiling Xiao
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Xiaoliang Ma
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
| | - Yajia Gu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jinwei Qiang
- Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Yuan H, Xu X, Tu S, Chen B, Wei Y, Ma Y. The CT-based intratumoral and peritumoral machine learning radiomics analysis in predicting lymph node metastasis in rectal carcinoma. BMC Gastroenterol 2022; 22:463. [DOI: 10.1186/s12876-022-02525-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 09/22/2022] [Accepted: 09/29/2022] [Indexed: 11/17/2022] Open
Abstract
Abstract
Background
To construct clinical and machine learning nomogram for predicting the lymph node metastasis (LNM) status of rectal carcinoma (RC) based on radiomics and clinical characteristics.
Methods
788 RC patients were enrolled from January 2015 to January 2021, including 303 RCs with LNM and 485 RCs without LNM. The radiomics features were calculated and selected with the methods of variance, correlation analysis, and gradient boosting decision tree. After feature selection, the machine learning algorithms of Bayes, k-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and decision tree (DT) were used to construct prediction models. The clinical characteristics combined with intratumoral and peritumoral radiomics was taken to develop a radiomics and machine learning nomogram. The relative standard deviation (RSD) was used to predict the stability of machine learning algorithms. The area under curves (AUCs) with 95% confidence interval (CI) were calculated to evaluate the predictive efficacy of all models.
Results
To intratumoral radiomics analysis, the RSD of Bayes was minimal compared with other four machine learning algorithms. The AUCs of arterial-phase based intratumoral Bayes model (0.626 and 0.627) were higher than these of unenhanced-phase and venous-phase ones in both the training and validation group.The AUCs of intratumoral and peritumoral Bayes model were 0.656 in the training group and were 0.638 in the validation group, and the relevant Bayes-score was quantified. The clinical-Bayes nomogram containing significant clinical variables of diameter, PNI, EMVI, CEA, and CA19-9, and Bayes-score was constructed. The AUC (95%CI), specificity, and sensitivity of this nomogram was 0.828 (95%CI, 0.800-0.854), 74.85%, and 77.23%.
Conclusion
Intratumoral and peritumoral radiomics can help predict the LNM status of RCs. The machine learning algorithm of Bayes in arterial-phase conducted better in consideration of terms of RSD and AUC. The clinical-Bayes nomogram achieved a better performance in predicting the LNM status of RCs.
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Mao Q, Zhou MT, Zhao ZP, Liu N, Yang L, Zhang XM. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J Gastroenterol 2022; 28:6002-6016. [PMID: 36405385 PMCID: PMC9669820 DOI: 10.3748/wjg.v28.i42.6002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 09/24/2022] [Accepted: 10/27/2022] [Indexed: 11/10/2022] Open
Abstract
Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.
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Affiliation(s)
- Qi Mao
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Mao-Ting Zhou
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Zhang-Ping Zhao
- Department of Radiology, Panzhihua Central Hospital, Panzhihua 617000, Sichuan Province, China
| | - Ning Liu
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Lin Yang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
| | - Xiao-Ming Zhang
- Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
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Song G, Li P, Wu R, Jia Y, Hong Y, He R, Li J, Zhang R, Li A. Development and validation of a high-resolution T2WI-based radiomic signature for the diagnosis of lymph node status within the mesorectum in rectal cancer. Front Oncol 2022; 12:945559. [PMID: 36185279 PMCID: PMC9523667 DOI: 10.3389/fonc.2022.945559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose The aim of this study was to explore the feasibility of a high-resolution T2-weighted imaging (HR-T2WI)-based radiomics prediction model for diagnosing metastatic lymph nodes (LNs) within the mesorectum in rectal cancer. Method A total of 604 LNs (306 metastatic and 298 non-metastatic) from 166 patients were obtained. All patients underwent HR-T2WI examination and total mesorectal excision (TME) surgery. Four kinds of segmentation methods were used to select region of interest (ROI), including method 1 along the border of LNs; method 2 along the expanded border of LNs with an additional 2–3 mm; method 3 covering the border of LNs only; and method 4, a circle region only within LNs. A total of 1,409 features were extracted for each method. Variance threshold method, Select K Best, and Lasso algorithm were used to reduce the dimension. All LNs were divided into training and test sets. Fivefold cross-validation was used to build the logistic model, which was evaluated by the receiver operating characteristic (ROC) with four indicators, including area under the curve (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). Three radiologists with different working experience in diagnosing rectal diseases assessed LN metastasis respectively. The diagnostic efficiencies with each of four segmentation methods and three radiologists were compared to each other. Results For the test set, the AUCs of four segmentation methods were 0.820, 0.799, 0.764, and 0.741; the ACCs were 0.725, 0.704, 0.709, and 0.670; the SEs were 0.756, 0.634, 0.700, and 0.589; and the SPs were 0.696, 0.772, 0.717, and 0.750, respectively. There was no statistically significant difference in AUC between the four methods (p > 0.05). Method 1 had the highest values of AUC, ACC, and SE. For three radiologists, the overall diagnostic efficiency was moderate. The corresponding AUCs were 0.604, 0.634, and 0.671; the ACCs were 0.601, 0.632, and 0.667; the SEs were 0.366, 0.552, and 0.392; and the SPs were 0.842, 0.715, and 0.950, respectively. Conclusions The proposed HR-T2WI-based radiomic signature exhibited a robust performance on predicting mesorectal LN status and could potentially be used for clinicians in order to determine the status of metastatic LNs in rectal cancer patients.
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Affiliation(s)
- Gesheng Song
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Panpan Li
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Rui Wu
- Department of Radiology, Shandong University, Jinan, China
| | - Yuping Jia
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Yu Hong
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
| | - Rong He
- Department of Radiology, The Shandong First Medical University, Jinan, China
| | - Jinye Li
- Department of Radiology, Shandong Provincial ENT Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ran Zhang
- Marketing, Medical Technology Co., Ltd., Beijing, China
| | - Aiyin Li
- Department of Radiology, The First Affiliated Hospital of Shandong First Medical University, Jinan, China
- *Correspondence: Aiyin Li,
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Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
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Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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Crimì F, Zanon C, Cabrelle G, Luong KD, Albertoni L, Bao QR, Borsetto M, Baratella E, Capelli G, Spolverato G, Fassan M, Pucciarelli S, Quaia E. Contrast-Enhanced CT Texture Analysis in Colon Cancer: Correlation with Genetic Markers. Tomography 2022; 8:2193-2201. [PMID: 36136880 PMCID: PMC9498512 DOI: 10.3390/tomography8050184] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/24/2022] [Accepted: 08/30/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The purpose of the study was to determine whether contrast-enhanced CT texture features relate to, and can predict, the presence of specific genetic mutations involved in CRC carcinogenesis. Materials and methods: This retrospective study analyzed the pre-operative CT in the venous phase of patients with CRC, who underwent testing for mutations in the KRAS, NRAS, BRAF, and MSI genes. Using a specific software based on CT images of each patient, for each slice including the tumor a region of interest was manually drawn along the margin, obtaining the volume of interest. A total of 56 texture parameters were extracted that were compared between the wild-type gene group and the mutated gene group. A p-value of <0.05 was considered statistically significant. Results: The study included 47 patients with stage III-IV CRC. Statistically significant differences between the MSS group and the MSI group were found in four parameters: GLRLM RLNU (area under the curve (AUC) 0.72, sensitivity (SE) 77.8%, specificity (SP) 65.8%), GLZLM SZHGE (AUC 0.79, SE 88.9%, SP 65.8%), GLZLM GLNU (AUC 0.74, SE 88.9%, SP 60.5%), and GLZLM ZLNU (AUC 0.77, SE 88.9%, SP 65.8%). Conclusions: The findings support the potential role of the CT texture analysis in detecting MSI in CRC based on pre-treatment CT scans.
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Affiliation(s)
- Filippo Crimì
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
| | - Chiara Zanon
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
| | - Giulio Cabrelle
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
| | - Kim Duyen Luong
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
| | - Laura Albertoni
- Pathology and Cytopathology Unit, Department of Medicine, University of Padova, 35128 Padua, Italy
| | - Quoc Riccardo Bao
- General Surgery 3, Department of Surgical, Oncological, and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy
| | - Marta Borsetto
- General Surgery 3, Department of Surgical, Oncological, and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy
| | - Elisa Baratella
- Department of Radiology, Cattinara Hospital, University of Trieste, 34127 Trieste, Italy
| | - Giulia Capelli
- General Surgery 3, Department of Surgical, Oncological, and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy
| | - Gaya Spolverato
- General Surgery 3, Department of Surgical, Oncological, and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy
| | - Matteo Fassan
- Pathology and Cytopathology Unit, Department of Medicine, University of Padova, 35128 Padua, Italy
- Veneto Institute of Oncology, IOV-IRCCS, 35128 Padua, Italy
| | - Salvatore Pucciarelli
- General Surgery 3, Department of Surgical, Oncological, and Gastroenterological Sciences (DiSCOG), University of Padova, 35128 Padova, Italy
| | - Emilio Quaia
- Institute of Radiology, Department of Medicine-DIMED, University of Padova, 35128 Padova, Italy
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Hong EK, Chalabi M, Landolfi F, Castagnoli F, Park SJ, Sikorska K, Aalbers A, van den Berg J, van Leerdam M, Lee JM, Beets-Tan R. Colon cancer CT staging according to mismatch repair status: Comparison and suggestion of imaging features for high-risk colon cancer. Eur J Cancer 2022; 174:165-175. [PMID: 36029713 DOI: 10.1016/j.ejca.2022.06.060] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 06/20/2022] [Accepted: 06/25/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND Neoadjuvant treatment with either chemotherapy or immunotherapy is gaining momentum in colon cancers (CC). To reduce over-treatment, increasing staging accuracy using computed tomography (CT) is of high importance. PURPOSE To assess and compare CT imaging features of CC between mismatch repair-proficient (pMMR) and MMR-deficient (dMMR) tumours and identify CT features that can distinguish high-risk (pT3-4, N+) CC according to MMR status. METHODS Primary staging CTs of 266 patients who underwent primary surgical resection of a colon tumour were retrospectively and independently evaluated by two radiologists. Logistic regression analysis was performed to identify significant associations between imaging features and positive lymph node status. Receiver operating characteristic (ROC) curves of significantly associated features were assessed and validated in an external cohort of 104 patients. RESULTS Among pT3 tumours only, dMMR CC were significantly larger than pMMR CC in both length and thickness (length 59.39 ± 26.28 mm versus 48.70 ± 23.72, respectively, p = 0.031; thickness 20.54 mm ± 11.17 versus 16.34 ± 8.73, respectively, p = 0.027). For pMMR tumours, nodal internal heterogeneity on CT was significantly associated with a positive lymph node status (odds ratio (OR) = 2.66, p = 0.027), while for dMMR tumours, the largest short diameter of the nodes was associated with lymph node status (OR = 2.01, p = 0.049). The best cut-off value of the largest short diameter of involved nodes was 10.4 mm for dMMR and 7.95 mm for pMMR. In the external validation cohort, AUCs for predicting involved nodes based on the largest short diameter was 0.764 for dMMR tumours using 10 mm size cut-off and 0.624 for pMMR tumours using 7 mm cut-off. CONCLUSION These data show that CT imaging features of primary CC differ between dMMR and pMMR tumours, suggesting that the assessment of CT-based CC staging should take MMR status into consideration, especially for lymph node status, and thus may help in selecting patients for neoadjuvant treatment.
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Affiliation(s)
- Eun Kyoung Hong
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands; Seoul National University Hospital, Seoul, South Korea; GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Myriam Chalabi
- Department of Gastrointestinal Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands.
| | - Federica Landolfi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands; Radiology Unit, Sapienza University of Rome, Sant'Andrea Hospital, Rome, Italy
| | - Francesca Castagnoli
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands; Department of Radiology, University of Brescia, Brescia, Italy
| | - Sae Jin Park
- Seoul National University Hospital, Seoul, South Korea
| | - Karolina Sikorska
- Department of Biostatistics, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Arend Aalbers
- Department of Surgery, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Jose van den Berg
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Monique van Leerdam
- Department of Gastrointestinal Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, the Netherlands; GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, the Netherlands.
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Yuan H, Peng Y, Xu X, Tu S, Wei Y, Ma Y. A Tumoral and Peritumoral CT-Based Radiomics and Machine Learning Approach to Predict the Microsatellite Instability of Rectal Carcinoma. Cancer Manag Res 2022; 14:2409-2418. [PMID: 35971393 PMCID: PMC9375564 DOI: 10.2147/cmar.s377138] [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: 06/08/2022] [Accepted: 07/29/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To predict the status of microsatellite instability (MSI) of rectal carcinoma (RC) using different machine learning algorithms based on tumoral and peritumoral radiomics combined with clinicopathological characteristics. Methods There were 497 RC patients enrolled in this retrospective study. The tumoral and peritumoral CT-based radiomic features were calculated after tumor segmentation. The radiomic features from two radiologists were compared by way of inter-observer correlation coefficient (ICC). After methods of variance, correlation, and dimension reduction, six machine learning algorithms of logistic regression (LR), Bayes, support vector machine, random forest, k-nearest neighbor, and decision tree were conducted to develop models for predicting MSI status of RC. The relative standard deviation (RSD) was quantified. The radiomics and significant clinicopathological variables constituted the radiomics-clinicopathological nomogram. The receiver operator curve (ROC) was made by DeLong test, and the area under curve (AUC) with 95% confidence interval (95% CI) was calculated to evaluate the performance of the model. Results The venous phase of CT examination was selected for further analysis because the proportion of radiomic features with ICC greater than 0.75 was higher. The tumoral and peritumoral model by LR algorithm (M-LR) with minimal RSD showed good performance in predicting MSI status of RC with the AUCs of 0.817 and 0.726 in the training and validation set. The radiomic-clinicopathological nomogram performed better in both the training and validation set with AUCs of 0.843 and 0.737. Conclusion The radiomics-clinicopathological nomogram demonstrated better predictive performance in evaluating the MSI status of RC.
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Affiliation(s)
- Hang Yuan
- Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China
| | - Yu Peng
- Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China
| | - Xiren Xu
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China
| | - Shiliang Tu
- Department of Colorectal Surgery, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China
| | - Yuguo Wei
- GE Healthcare, Precision Health Institution, Hangzhou, People's Republic of China
| | - Yanqing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, People's Republic of China
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Predicting Mismatch-Repair Status in Rectal Cancer Using Multiparametric MRI-Based Radiomics Models: A Preliminary Study. BIOMED RESEARCH INTERNATIONAL 2022; 2022:6623574. [PMID: 36033579 PMCID: PMC9400426 DOI: 10.1155/2022/6623574] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/02/2022] [Indexed: 12/24/2022]
Abstract
Detecting mismatch-repair (MMR) status is crucial for personalized treatment strategies and prognosis in rectal cancer (RC). A preoperative, noninvasive, and cost-efficient predictive tool for MMR is critically needed. Therefore, this study developed and validated machine learning radiomics models for predicting MMR status in patients directly on preoperative MRI scans. Pathologically confirmed RC cases administered surgical resection in two distinct hospitals were examined in this retrospective trial. Totally, 78 and 33 cases were included in the training and test sets, respectively. Then, 65 cases were enrolled as an external validation set. Radiomics features were obtained from preoperative rectal MR images comprising T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI), and combined multisequences. Four optimal features related to MMR status were selected by the least absolute shrinkage and selection operator (LASSO) method. Support vector machine (SVM) learning was adopted to establish four predictive models, i.e., ModelT2WI, ModelDWI, ModelCE-T1WI, and Modelcombination, whose diagnostic performances were determined and compared by receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Modelcombination had better diagnostic performance compared with the other models in all datasets (all p < 0.05). The usefulness of the proposed model was confirmed by DCA. Therefore, the present pilot study showed the radiomics model combining multiple sequences derived from preoperative MRI is effective in predicting MMR status in RC cases.
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Detection of Microsatellite Instability in Colonoscopic Biopsies and Postal Urine Samples from Lynch Syndrome Cancer Patients Using a Multiplex PCR Assay. Cancers (Basel) 2022; 14:cancers14153838. [PMID: 35954501 PMCID: PMC9367254 DOI: 10.3390/cancers14153838] [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: 05/31/2022] [Revised: 07/15/2022] [Accepted: 07/21/2022] [Indexed: 11/16/2022] Open
Abstract
Identification of mismatch repair (MMR)-deficient colorectal cancers (CRCs) is recommended for Lynch syndrome (LS) screening, and supports targeting of immune checkpoint inhibitors. Microsatellite instability (MSI) analysis is commonly used to test for MMR deficiency. Testing biopsies prior to tumour resection can inform surgical and therapeutic decisions, but can be limited by DNA quantity. MSI analysis of voided urine could also provide much needed surveillance for genitourinary tract cancers in LS. Here, we reconfigure an existing molecular inversion probe-based MSI and BRAF c.1799T > A assay to a multiplex PCR (mPCR) format, and demonstrate that it can sample >140 unique molecules per marker from <1 ng of DNA and classify CRCs with 96−100% sensitivity and specificity. We also show that it can detect increased MSI within individual and composite CRC biopsies from LS patients, and within preoperative urine cell free DNA (cfDNA) from two LS patients, one with an upper tract urothelial cancer, the other an undiagnosed endometrial cancer. Approximately 60−70% of the urine cfDNAs were tumour-derived. Our results suggest that mPCR sequence-based analysis of MSI and mutation hotspots in CRC biopsies could facilitate presurgery decision making, and could enable postal-based screening for urinary tract and endometrial tumours in LS patients.
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Hong EK, Bodalal Z, Landolfi F, Bogveradze N, Bos P, Park SJ, Lee JM, Beets-Tan R. Identifying high-risk colon cancer on CT an a radiomics signature improve radiologist's performance for T staging? ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:2739-2746. [PMID: 35661244 DOI: 10.1007/s00261-022-03534-0] [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] [Received: 01/03/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 01/18/2023]
Abstract
PURPOSE To assess the role of radiomics in detection of high-risk (pT3-4) colon cancer and develop a combined model that combines both radiomics and CT staging of colon cancer. METHODS We included 292 colon cancer patients who underwent pre-operative CT and primary surgical resection within 2 months. Three-dimensional segmentations and CT staging of primary colon tumors were done. From each 3D segmentation of colon tumor, radiomic features were automatically extracted. Logistic regression analysis was performed to identify associations between radiomic features and high-risk (pT3-4) colon tumors. A combined model that integrated both radiomics and CT staging was developed and their diagnostic performance was compared with that of conventional CT staging. Tenfold cross-validation was used to validate the performance of the model and CT staging. RESULTS The model that combined radiomic features and CT staging demonstrated a significantly better performance in detection of high-risk colon tumors in training set (AUC = 0.799, 95% CI: 0.720-0.839 for combined model and AUC = 0.697, 95% CI = 0.538-0.756 for CT staging only, p < 0.001 for difference). Cross-validation results also demonstrated significantly better detection performance of combined model (AUC = 0.727, 95% Confidence Interval (CI): 0.621-0.777 for combined model and AUC = 0.628, 95% CI = 0.558-0.689 for CT staging only, Boot CI = 0.099). CONCLUSION CT radiomic features of primary colon cancer, combined with CT staging, can improve the detection of high-risk colon cancer patients.
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Affiliation(s)
- Eun Kyoung Hong
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
- Seoul National University Hospital, Seoul, South Korea.
| | - Zuhir Bodalal
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Federica Landolfi
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Radiology Unit, Sant'Andrea Hospital, Sapienza University of Rome, Rome, Italy
| | - Nino Bogveradze
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- Academic Pridon Todua Medical Center, Research Institute of Clinical Medicine, Tbilisi, Georgia
| | - Paula Bos
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sae Jin Park
- Seoul National University Hospital, Seoul, South Korea
| | - Jeong Min Lee
- Seoul National University Hospital, Seoul, South Korea
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
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Caruso D, Polici M, Zerunian M, Del Gaudio A, Parri E, Giallorenzi MA, De Santis D, Tarantino G, Tarallo M, Dentice di Accadia FM, Iannicelli E, Garbarino GM, Canali G, Mercantini P, Fiori E, Laghi A. Radiomic Cancer Hallmarks to Identify High-Risk Patients in Non-Metastatic Colon Cancer. Cancers (Basel) 2022; 14:3438. [PMID: 35884499 PMCID: PMC9319440 DOI: 10.3390/cancers14143438] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 07/07/2022] [Accepted: 07/13/2022] [Indexed: 11/16/2022] Open
Abstract
The study was aimed to develop a radiomic model able to identify high-risk colon cancer by analyzing pre-operative CT scans. The study population comprised 148 patients: 108 with non-metastatic colon cancer were retrospectively enrolled from January 2015 to June 2020, and 40 patients were used as the external validation cohort. The population was divided into two groups—High-risk and No-risk—following the presence of at least one high-risk clinical factor. All patients had baseline CT scans, and 3D cancer segmentation was performed on the portal phase by two expert radiologists using open-source software (3DSlicer v4.10.2). Among the 107 radiomic features extracted, stable features were selected to evaluate the inter-class correlation (ICC) (cut-off ICC > 0.8). Stable features were compared between the two groups (T-test or Mann−Whitney), and the significant features were selected for univariate and multivariate logistic regression to build a predictive radiomic model. The radiomic model was then validated with an external cohort. In total, 58/108 were classified as High-risk and 50/108 as No-risk. A total of 35 radiomic features were stable (0.81 ≤ ICC < 0.92). Among these, 28 features were significantly different between the two groups (p < 0.05), and only 9 features were selected to build the radiomic model. The radiomic model yielded an AUC of 0.73 in the internal cohort and 0.75 in the external cohort. In conclusion, the radiomic model could be seen as a performant, non-invasive imaging tool to properly stratify colon cancers with high-risk disease.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Antonella Del Gaudio
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Emanuela Parri
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Maria Agostina Giallorenzi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Domenico De Santis
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Giulia Tarantino
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy; (M.T.); (F.M.D.d.A.); (E.F.)
| | | | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
| | - Giovanni Maria Garbarino
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Giulia Canali
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Paolo Mercantini
- Surgery Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (G.T.); (G.M.G.); (G.C.); (P.M.)
| | - Enrico Fiori
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy; (M.T.); (F.M.D.d.A.); (E.F.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (M.P.); (M.Z.); (A.D.G.); (E.P.); (M.A.G.); (D.D.S.); (E.I.); (A.L.)
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Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:e004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient's outcomes and to assess response to these promising combinations.
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Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
- INSERM U1030, Gustave Roussy, Villejuif, France
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40
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Quraishi MI. Radiomics-Guided Precision Medicine Approaches for Colorectal Cancer. Front Oncol 2022; 12:872656. [PMID: 35756680 PMCID: PMC9218262 DOI: 10.3389/fonc.2022.872656] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 05/06/2022] [Indexed: 11/13/2022] Open
Abstract
The concept of precision oncology entails molecular profiling of tumors to guide therapeutic interventions. Genomic testing through next-generation sequencing (NGS) molecular analysis provides the basis of such highly targeted therapeutics in oncology. As radiomic analysis delivers an array of structural and functional imaging-based biomarkers that depict these molecular mechanisms and correlate with key genetic alterations related to cancers. There is an opportunity to synergize these two big-data approaches to determine the molecular guidance for precision therapeutics. Colorectal cancer is one such disease whose therapeutic management is being guided by genetic and genomic analyses. We review the rationale and utility of radiomics as a combinative strategy for these approaches in the management of colorectal cancer.
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Affiliation(s)
- Mohammed I Quraishi
- Department of Radiology, University of Tennessee Medical Center, Knoxville, TN, United States
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41
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Colon cancer microsatellite instability influences computed tomography assessment of regional lymph node morphology and diagnostic performance. Eur J Radiol 2022; 154:110396. [PMID: 35709643 DOI: 10.1016/j.ejrad.2022.110396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/24/2022] [Accepted: 06/03/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To elucidate whether a high level of microsatellite instability (MSI-high) in colon cancer influences the CT assessment of regional lymph node (rLN) morphology and diagnostic performance on predicting pathological node-negative (pN0) patients. METHOD We retrospectively reviewed 507 patients with cecal/proximal ascending colon cancer (age, 63.0 ± 11.6 years; MSI-stable, n = 398; MSI-high, n = 109) who underwent right hemicolectomy between July 1, 2009, and December 31, 2018. Preoperative CT images were assessed for number of rLNs, long/short diameter of the largest rLN, and CT LN grade (CTN0, low probability of metastasis; CTN1, borderline; CTN2, high probability). Sensitivity, specificity, positive predictive value and negative predictive value for predicting pN0 was calculated. Multivariable logistic regression analysis was performed. Statistical significance was defined as P < 0.05. RESULTS A study population of 507 patients (mean age ± standard deviation, 63.0 ± 11.6; 264 women) were evaluated. In patients with rLN metastasis, the rLN long axis (pN1: P = 0.013, pN2: P = 0.039) and short axis (pN1: P = 0.001, pN2: P = 0.009) were both longer in MSI-high tumors compared with MSI-stable tumors. High specificity for predicting pN0 was only achieved in MSI-high tumors [sensitivityMSI-stable = 58.3% (n = 137/235), specificityMSI-stable = 71.2% (n = 116/163); sensitivityMSI-high = 38.4% (n = 33/86), specificityMSI-high = 91.3% (n = 21/23)]. Multivariable logistic regression indicated MSI-high (P < 0.001, odds ratio = 3.701), smaller LN long axis (P = 0.023, odds ratio = 0.905), and lower CT LN grade (CTN0: P = 0.009, odds ratio = 2.987; CTN1: P = 0.014, odds ratio = 2.195) as significant parameters in predicting pN0. CONCLUSION MSI-high colon cancer is associated with larger rLNs and high specificity for predicting pN0 on CT assessment.
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Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
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Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
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Ying M, Pan J, Lu G, Zhou S, Fu J, Wang Q, Wang L, Hu B, Wei Y, Shen J. Development and validation of a radiomics-based nomogram for the preoperative prediction of microsatellite instability in colorectal cancer. BMC Cancer 2022; 22:524. [PMID: 35534797 PMCID: PMC9087961 DOI: 10.1186/s12885-022-09584-3] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/21/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Preoperative prediction of microsatellite instability (MSI) status in colorectal cancer (CRC) patients is of great significance for clinicians to perform further treatment strategies and prognostic evaluation. Our aims were to develop and validate a non-invasive, cost-effective reproducible and individualized clinic-radiomics nomogram method for preoperative MSI status prediction based on contrast-enhanced CT (CECT)images. METHODS A total of 76 MSI CRC patients and 200 microsatellite stability (MSS) CRC patients with pathologically confirmed (194 in the training set and 82 in the validation set) were identified and enrolled in our retrospective study. We included six significant clinical risk factors and four qualitative imaging data extracted from CECT images to build the clinics model. We applied the intra-and inter-class correlation coefficient (ICC), minimal-redundancy-maximal-relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) for feature reduction and selection. The selected independent prediction clinical risk factors, qualitative imaging data and radiomics features were performed to develop a predictive nomogram model for MSI status on the basis of multivariable logistic regression by tenfold cross-validation. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots and Hosmer-Lemeshow test were performed to assess the nomogram model. Finally, decision curve analysis (DCA) was performed to determine the clinical utility of the nomogram model by quantifying the net benefits of threshold probabilities. RESULTS Twelve top-ranked radiomics features, three clinical risk factors (location, WBC and histological grade) and CT-reported IFS were finally selected to construct the radiomics, clinics and combined clinic-radiomics nomogram model. The clinic-radiomics nomogram model with the highest AUC value of 0.87 (95% CI, 0.81-0.93) and 0.90 (95% CI, 0.83-0.96), as well as good calibration and clinical utility observed using the calibration plots and DCA in the training and validation sets respectively, was regarded as the candidate model for identification of MSI status in CRC patients. CONCLUSION The proposed clinic-radiomics nomogram model with a combination of clinical risk factors, qualitative imaging data and radiomics features can potentially be effective in the individualized preoperative prediction of MSI status in CRC patients and may help performing further treatment strategies.
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Affiliation(s)
- Mingliang Ying
- Department of Radiology, The Second Affiliated Hospital of Soochow University, No.1055 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China.,Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Jiangfeng Pan
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Guanghong Lu
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Shaobin Zhou
- Department of Radiology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Jianfei Fu
- Department of Oncology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Qinghua Wang
- Department of Oncology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Lixia Wang
- Department of Pathology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Bin Hu
- Department of Pathology, Jinhua Hospital of Zhejiang University: Jinhua Municipal Central Hospital, No. 351 Mingyue Road, Jinhua, Zhejiang, China
| | - Yuguo Wei
- Precision Health Institution, GE Healthcare, Xihu District, Hangzhou, China
| | - Junkang Shen
- Department of Radiology, The Second Affiliated Hospital of Soochow University, No.1055 Sanxiang Road, Gusu District, Suzhou, 215004, Jiangsu, China. .,Institute of Radiation Oncology Therapeutics of Soochow University, Suzhou, 215004, China.
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Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2022; 19:132-146. [PMID: 34663898 PMCID: PMC9034765 DOI: 10.1038/s41571-021-00560-7] [Citation(s) in RCA: 391] [Impact Index Per Article: 130.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2021] [Indexed: 12/14/2022]
Abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Tempus Labs, Chicago, IL, USA
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH, USA.
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Multi-Omic Approaches in Colorectal Cancer beyond Genomic Data. J Pers Med 2022; 12:jpm12020128. [PMID: 35207616 PMCID: PMC8880341 DOI: 10.3390/jpm12020128] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/10/2022] [Accepted: 01/12/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most frequent tumours and one of the major causes of morbidity and mortality globally. Its incidence has increased in recent years and could be linked to unhealthy dietary habits combined with environmental and hereditary factors, which can lead to genetic and epigenetic changes and induce tumour development. The model of CRC progression has always been based on a genomic, parametric, static and complex approach involving oncogenes and tumour suppressor genes. Recent advances in omics sciences have sought a paradigm shift to a multiparametric, immunological-stromal, and dynamic approach for a better understanding of carcinogenesis and tumour heterogeneity. In the present paper, we review the most important preclinical and clinical data and present recent discoveries in the field of transcriptomics, proteomics, metagenomics and radiomics in CRC disease.
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Wang Y, Ma LY, Yin XP, Gao BL. Radiomics and Radiogenomics in Evaluation of Colorectal Cancer Liver Metastasis. Front Oncol 2022; 11:689509. [PMID: 35070948 PMCID: PMC8776634 DOI: 10.3389/fonc.2021.689509] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
Colorectal cancer is one common digestive malignancy, and the most common approach of blood metastasis of colorectal cancer is through the portal vein system to the liver. Early detection and treatment of liver metastasis is the key to improving the prognosis of the patients. Radiomics and radiogenomics use non-invasive methods to evaluate the biological properties of tumors by deeply mining the texture features of images and quantifying the heterogeneity of metastatic tumors. Radiomics and radiogenomics have been applied widely in the detection, treatment, and prognostic evaluation of colorectal cancer liver metastases. Based on the imaging features of the liver, this paper reviews the current application of radiomics and radiogenomics in the diagnosis, treatment, monitor of disease progression, and prognosis of patients with colorectal cancer liver metastases.
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Affiliation(s)
| | | | - Xiao-Ping Yin
- CT-MRI Room, Affiliated Hospital of Hebei University, Baoding, China
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Kang CY, Duarte SE, Kim HS, Kim E, Park J, Lee AD, Kim Y, Kim L, Cho S, Oh Y, Gim G, Park I, Lee D, Abazeed M, Velichko YS, Chae YK. OUP accepted manuscript. Oncologist 2022; 27:e471-e483. [PMID: 35348765 PMCID: PMC9177100 DOI: 10.1093/oncolo/oyac036] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/14/2022] [Indexed: 11/17/2022] Open
Abstract
The recent, rapid advances in immuno-oncology have revolutionized cancer treatment and spurred further research into tumor biology. Yet, cancer patients respond variably to immunotherapy despite mounting evidence to support its efficacy. Current methods for predicting immunotherapy response are unreliable, as these tests cannot fully account for tumor heterogeneity and microenvironment. An improved method for predicting response to immunotherapy is needed. Recent studies have proposed radiomics—the process of converting medical images into quantitative data (features) that can be processed using machine learning algorithms to identify complex patterns and trends—for predicting response to immunotherapy. Because patients undergo numerous imaging procedures throughout the course of the disease, there exists a wealth of radiological imaging data available for training radiomics models. And because radiomic features reflect cancer biology, such as tumor heterogeneity and microenvironment, these models have enormous potential to predict immunotherapy response more accurately than current methods. Models trained on preexisting biomarkers and/or clinical outcomes have demonstrated potential to improve patient stratification and treatment outcomes. In this review, we discuss current applications of radiomics in oncology, followed by a discussion on recent studies that use radiomics to predict immunotherapy response and toxicity.
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Affiliation(s)
| | | | - Hye Sung Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Eugene Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Alice Daeun Lee
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Yeseul Kim
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Leeseul Kim
- Department of Internal Medicine, AMITA Health Saint Francis Hospital, Evanston, IL, USA
| | - Sukjoo Cho
- Department of Pediatrics, University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Yoojin Oh
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gahyun Gim
- Department of Hematology and Oncology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA
| | - Inae Park
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Dongyup Lee
- Department of Physical Medicine and Rehabilitation, Geisinger Health System, Danville, PA, USA
| | - Mohamed Abazeed
- Department of Radiation Oncology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Yury S Velichko
- Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Young Kwang Chae
- Corresponding author: Young Kwang Chae, Department of Hematology and Oncology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Whole Exome Sequencing Identifies Two Novel Mutations in a Patient with UC Associated with PSC and SSA. Can J Gastroenterol Hepatol 2021; 2021:9936932. [PMID: 34545326 PMCID: PMC8449715 DOI: 10.1155/2021/9936932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/24/2021] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Patients diagnosed with ulcerative colitis (UC) associated with primary sclerosis cholangitis (PSC) and sessile serrated adenoma (SSA) are rare. The present study aimed to identify the potential causative gene mutation in a patient with UC associated with PSC and SSA. METHODS DNA was extracted from the blood sample and tissue sample of SSA, followed by the whole exome sequencing (WES) analysis. Bioinformatics analysis was utilized to predict the deleteriousness of the identified variants. Multiple sequence alignment and conserved protein domain analyses were performed using online software. Sanger sequencing was used to validate the identified variants. Expression and diagnostic analysis of identified mutated genes was performed in the GSE119600 dataset (peripheral blood samples of PSC and UC) and GSE43841 dataset (tumor samples of SSA). RESULTS In the present study, a total of 842 single nucleotide variants (SNVs) in 728 genes were identified in the blood sample. Two variants, integrin beta 4 (ITGB4) (c.C2503G; p.P835A) and a mucin 3A (MUC3A) (c.C1019T; p.P340L), were further analyzed. MUC3A was associated with inflammatory bowel disease. Sanger sequence in blood revealed that the ITGB4 mutation was fully cosegregated with the result of WES in the patient. Additionally, a variant, tumor protein p53 gene (TP53) (c.86delA; p.N29Tfs ∗ 15) was identified in the tissue sample of SSA. Compared to that in normal controls, ITGB4 was upregulated in both UC and PSC, MUC3A was, respectively, upregulated and downregulated in PSC and UC, and TP53 was downregulated in SSA. ITGB4 and TP53 had a potential diagnostic value for UC, PSC and SSA. CONCLUSIONS The present study demonstrated that the ITGB4 (c.C2503G; p.P835A) and MUC3A (c.C1019T; p.P340L) mutations may be the potential causative variants in a patient with UC associated with PSC and SSA. TP53 (c.86delA; p.N29Tfs ∗ 15) mutation may be associated with SSA in this patient.
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Stetson PD, Cantor MN, Gonen M. When Predictive Models Collide. JCO Clin Cancer Inform 2021; 4:547-550. [PMID: 32543898 DOI: 10.1200/cci.20.00024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Affiliation(s)
- Peter D Stetson
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Michael N Cantor
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Mithat Gonen
- Department of Medicine, Digital Informatics and Technology Solutions, Memorial Sloan Kettering Cancer Center, New York, NY
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Li J, Yang Z, Xin B, Hao Y, Wang L, Song S, Xu J, Wang X. Quantitative Prediction of Microsatellite Instability in Colorectal Cancer With Preoperative PET/CT-Based Radiomics. Front Oncol 2021; 11:702055. [PMID: 34367985 PMCID: PMC8339969 DOI: 10.3389/fonc.2021.702055] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/30/2021] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVES Microsatellite instability (MSI) status is an important hallmark for prognosis prediction and treatment recommendation of colorectal cancer (CRC). To address issues due to the invasiveness of clinical preoperative evaluation of microsatellite status, we investigated the value of preoperative 18F-FDG PET/CT radiomics with machine learning for predicting the microsatellite status of colorectal cancer patients. METHODS A total of 173 patients that underwent 18F-FDG PET/CT scans before operations were retrospectively analyzed in this study. The microsatellite status for each patient was identified as microsatellite instability-high (MSI-H) or microsatellite stable (MSS), according to the test for mismatch repair gene proteins with immunohistochemical staining methods. There were 2,492 radiomic features in total extracted from 18F-FDG PET/CT imaging. Then, radiomic features were selected through multivariate random forest selection and univariate relevancy tests after handling the imbalanced dataset through the random under-sampling method. Based on the selected features, we constructed a BalancedBagging model based on Adaboost classifiers to identify the MSI status in patients with CRC. The model performance was evaluated by the area under the curve (AUC), sensitivity, specificity, and accuracy on the validation dataset. RESULTS The ensemble model was constructed based on two radiomic features and achieved an 82.8% AUC for predicting the MSI status of colorectal cancer patients. The sensitivity, specificity, and accuracy were 83.3, 76.3, and 76.8%, respectively. The significant correlation of the selected two radiomic features with multiple effective clinical features was identified (p < 0.05). CONCLUSION 18F-FDG PET/CT radiomics analysis with the machine learning model provided a quantitative, efficient, and non-invasive mechanism for identifying the microsatellite status of colorectal cancer patients, which optimized the treatment decision support.
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Affiliation(s)
- Jiaru Li
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Ziyi Yang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Yichao Hao
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Lisheng Wang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Junyan Xu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
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