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For: Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018;29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]

For:	Sandrasegaran K, Lin Y, Asare-Sawiri M, Taiyini T, Tann M. CT texture analysis of pancreatic cancer. Eur Radiol 2018;29:1067-1073. [PMID: 30116961 DOI: 10.1007/s00330-018-5662-1] [Citation(s) in RCA: 106] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 06/15/2018] [Accepted: 07/13/2018] [Indexed: 02/07/2023]

Number

Cited by Other Article(s)

Barat M, Greffier J, Si-Mohamed S, Dohan A, Pellat A, Frandon J, Calame P, Soyer P. CT Imaging of the Pancreas: A Review of Current Developments and Applications. Can Assoc Radiol J 2025:8465371251319965. [PMID: 39985297 DOI: 10.1177/08465371251319965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2025] Open

Belfrage H, Kuuliala K, Kuuliala A, Mustonen H, Puolakkainen P, Kylänpää L, Seppänen H, Louhimo J. Circulating necroptosis markers in chronic pancreatitis and pancreatic cancer: Associations with diagnosis and prognostic factors. Pancreatology 2024;24:1229-1236. [PMID: 39613683 DOI: 10.1016/j.pan.2024.11.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2024] [Revised: 11/21/2024] [Accepted: 11/23/2024] [Indexed: 12/01/2024]

Bülbül HM, Burakgazi G, Kesimal U, Kaba E. Radiomics-based machine learning in the differentiation of benign and malignant bowel wall thickening radiomics in bowel wall thickening. Jpn J Radiol 2024;42:872-879. [PMID: 38536559 DOI: 10.1007/s11604-024-01558-8] [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: 12/20/2023] [Accepted: 03/12/2024] [Indexed: 07/30/2024]

Abstract

PURPOSE

To distinguish malignant and benign bowel wall thickening (BWT) by using computed tomography (CT) texture features based on machine learning (ML) models and to compare its success with the clinical model and combined model.

METHODS

One hundred twenty-two patients with BWT identified on contrast-enhanced abdominal CT and underwent colonoscopy were included in this retrospective study. Texture features were extracted from CT images using LifeX software. Feature selection and reduction were performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Six radiomic features were selected with LASSO. In the clinical model, six features (age, gender, thickness, fat stranding, symmetry, and lymph node) were included. Six radiomic and six clinical features were used in the combined model. Classification was done using two machine learning algorithms: Support Vector Machine (SVM) and Logistic Regression (LR). The data sets were divided into 80% training set and 20% test set. Then, training took place with all three datasets. The model's success was tested with the test set consisting of features not used during training.

RESULTS

In the training set, the combined model had the best performance with the area under the curve (AUC) value of 0.99 for SVM and 0.95 for LR. In the radiomic-derived model, the AUC value is 0.87 in SVM and 0.79 in LR. In the clinical model, SVM made this distinction with 0.95 AUC and LR with 0.92 AUC value. In the test set, the classifier with the highest success distinguishing malignant wall thickening is SVM in the radiomic-derived model with an AUC value of 0.90. In other models, the AUC value is in the range of 0.75-0.86, and the accuracy values are in the range of 0.72-0.84.

CONCLUSION

In conclusion, radiomic-based machine learning has shown high success in distinguishing malignant and benign BWT and may improve diagnostic accuracy compared to clinical features only. The results of our study may help ensure early diagnosis and treatment of colorectal cancers by facilitating the recognition of malignant BWT.

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Cai TN, Zhao L, Yang Y, Mao HM, Huang SG, Guo WL. Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction. Br J Radiol 2024;97:1029-1037. [PMID: 38460184 PMCID: PMC11075976 DOI: 10.1093/bjr/tqae054] [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: 10/14/2023] [Revised: 02/19/2024] [Accepted: 03/06/2024] [Indexed: 03/11/2024] Open

Yu C, Ruan Y, Yu L, Wang X, Hu Z, Zhu G, Huang T. Predicting postoperative prognosis of pancreatic cancer using a computed tomography-based radio-clinical model: exploring biologic functions. J Gastrointest Surg 2024;28:458-466. [PMID: 38583896 DOI: 10.1016/j.gassur.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/19/2024] [Accepted: 02/03/2024] [Indexed: 04/09/2024]

Mokhtari A, Casale R, Salahuddin Z, Paquier Z, Guiot T, Woodruff HC, Lambin P, Van Laethem JL, Hendlisz A, Bali MA. Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study. Diagnostics (Basel) 2024;14:712. [PMID: 38611625 PMCID: PMC11011556 DOI: 10.3390/diagnostics14070712] [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: 02/17/2024] [Revised: 03/11/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open

Abstract

PURPOSE

This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients.

METHODS

Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability.

RESULTS

A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70).

CONCLUSIONS

An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.

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Affiliation(s)

Ayoub Mokhtari Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Roberto Casale Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Zohaib Salahuddin Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands
Zelda Paquier Medical Physics Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Thomas Guiot Medical Physics Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Henry C. Woodruff Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229HX Maastricht, The Netherlands
Philippe Lambin Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229HX Maastricht, The Netherlands
Jean-Luc Van Laethem Department of Gastroenterology and Digestive Oncology, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Alain Hendlisz Department of Gastroenterology and Digestive Oncology, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
Maria Antonietta Bali Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium

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Bette S, Canalini L, Feitelson LM, Woźnicki P, Risch F, Huber A, Decker JA, Tehlan K, Becker J, Wollny C, Scheurig-Münkler C, Wendler T, Schwarz F, Kroencke T. Radiomics-Based Machine Learning Model for Diagnosis of Acute Pancreatitis Using Computed Tomography. Diagnostics (Basel) 2024;14:718. [PMID: 38611632 PMCID: PMC11011980 DOI: 10.3390/diagnostics14070718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024] Open

Abstract

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

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Affiliation(s)

Stefanie Bette Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Luca Canalini Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Laura-Marie Feitelson Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Piotr Woźnicki Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, University of Würzburg, 97080 Würzburg, Germany;
Franka Risch Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Adrian Huber Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Josua A. Decker Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Kartikay Tehlan Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Judith Becker Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Claudia Wollny Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Christian Scheurig-Münkler Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.)
Thomas Wendler Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.) Institute of Digital Health, University Hospital Augsburg, Faculty of Medicine, University of Augsburg, 86356 Neusaess, Germany Computer-Aided Medical Procedures and Augmented Reality, School of Computation, Information and Technology, Technical University of Munich, 85748 Garching bei Muenchen, Germany
Florian Schwarz Centre for Diagnostic Imaging and Interventional Therapy, Donau-Isar-Klinikum, 94469 Deggendorf, Germany;
Thomas Kroencke Clinic for Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Augsburg, 86156 Augsburg, Germany; (S.B.); (L.C.); (L.-M.F.); (A.H.); (J.A.D.); (K.T.); (J.B.); (C.W.); (C.S.-M.); (T.W.) Centre for Advanced Analytics and Predictive Sciences (CAAPS), University of Augsburg, 86159 Augsburg, Germany

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Awais M, Khan N, Khan AK, Rehman A. CT texture analysis for differentiating between peritoneal carcinomatosis and peritoneal tuberculosis: a cross-sectional study. Abdom Radiol (NY) 2024;49:857-867. [PMID: 37996544 DOI: 10.1007/s00261-023-04103-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/13/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023]

Li J, Ma Y, Yang C, Qiu G, Chen J, Tan X, Zhao Y. Radiomics analysis of R2* maps to predict early recurrence of single hepatocellular carcinoma after hepatectomy. Front Oncol 2024;14:1277698. [PMID: 38463221 PMCID: PMC10920317 DOI: 10.3389/fonc.2024.1277698] [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: 08/15/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open

Abstract

OBJECTIVES

This study aimed to evaluate the effectiveness of radiomics analysis with R2* maps in predicting early recurrence (ER) in single hepatocellular carcinoma (HCC) following partial hepatectomy.

METHODS

We conducted a retrospective analysis involving 202 patients with surgically confirmed single HCC having undergone preoperative magnetic resonance imaging between 2018 and 2021 at two different institutions. 126 patients from Institution 1 were assigned to the training set, and 76 patients from Institution 2 were assigned to the validation set. A least absolute shrinkage and selection operator (LASSO) regularization was conducted to operate a logistic regression, then features were identified to construct a radiomic score (Rad-score). Uni- and multi-variable tests were used to assess the correlations of clinicopathological features and Rad-score with ER. We then established a combined model encompassing the optimal Rad-score and clinical-pathological risk factors. Additionally, we formulated and validated a predictive nomogram for predicting ER in HCC. The nomogram's discrimination, calibration, and clinical utility were thoroughly evaluated.

RESULTS

Multivariable logistic regression revealed the Rad-score, microvascular invasion (MVI), and α fetoprotein (AFP) level > 400 ng/mL as significant independent predictors of ER in HCC. We constructed a nomogram based on these significant factors. The areas under the receiver operator characteristic curve of the nomogram and precision-recall curve were 0.901 and 0.753, respectively, with an F1 score of 0.831 in the training set. These values in the validation set were 0.827, 0.659, and 0.808.

CONCLUSION

The nomogram that integrates the radiomic score, MVI, and AFP demonstrates high predictive efficacy for estimating the risk of ER in HCC. It facilitates personalized risk classification and therapeutic decision-making for HCC patients.

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Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023;15:304-314. [PMID: 38058604 PMCID: PMC10696186 DOI: 10.4329/wjr.v15.i11.304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 09/20/2023] [Accepted: 10/23/2023] [Indexed: 11/24/2023] Open

Abstract

BACKGROUND

Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking.

AIM

To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients.

METHODS

We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis.

RESULTS

3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%.

CONCLUSION

Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.

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Litjens G, Broekmans JPEA, Boers T, Caballo M, van den Hurk MHF, Ozdemir D, van Schaik CJ, Janse MHA, van Geenen EJM, van Laarhoven CJHM, Prokop M, de With PHN, van der Sommen F, Hermans JJ. Computed Tomography-Based Radiomics Using Tumor and Vessel Features to Assess Resectability in Cancer of the Pancreatic Head. Diagnostics (Basel) 2023;13:3198. [PMID: 37892019 PMCID: PMC10606005 DOI: 10.3390/diagnostics13203198] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/01/2023] [Accepted: 10/11/2023] [Indexed: 10/29/2023] Open

Affiliation(s)

Geke Litjens Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Joris P. E. A. Broekmans Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Tim Boers Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Marco Caballo Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Maud H. F. van den Hurk Department of Plastic and Reconstructive Surgery, Saint Vincent’s University Hospital, D04 T6F4 Dublin, Ireland
Dilek Ozdemir Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Caroline J. van Schaik Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Markus H. A. Janse Image Sciences Institute, University Medical Center Utrecht, 3584 CX Utrecht, The Netherlands
Erwin J. M. van Geenen Department of Gastroenterology and Hepatology, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Cees J. H. M. van Laarhoven Department of Surgery, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Mathias Prokop Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
Peter H. N. de With Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
Fons van der Sommen Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, The Netherlands
John J. Hermans Department of Medical Imaging, Radboud Institute for Health Sciences, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands

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Sijithra PC, Santhi N, Ramasamy N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods. Eur J Radiol 2023;166:110972. [PMID: 37454557 DOI: 10.1016/j.ejrad.2023.110972] [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: 06/26/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]

Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LC. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging 2023;104:435-447. [PMID: 36967355 DOI: 10.1016/j.diii.2023.03.002] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]

Xiang F, He X, Liu X, Li X, Zhang X, Fan Y, Yan S. Development and Validation of a Nomogram for Preoperative Prediction of Early Recurrence after Upfront Surgery in Pancreatic Ductal Adenocarcinoma by Integrating Deep Learning and Radiological Variables. Cancers (Basel) 2023;15:3543. [PMID: 37509206 PMCID: PMC10377149 DOI: 10.3390/cancers15143543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/29/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open

Huang Y, Zhou S, Luo Y, Zou J, Li Y, Chen S, Gao M, Huang K, Lian G. Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients. Radiat Oncol 2023;18:79. [PMID: 37165440 PMCID: PMC10170860 DOI: 10.1186/s13014-023-02273-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open

Affiliation(s)

Yuzhou Huang Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
Shurui Zhou Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
Yanji Luo Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, No.58 Zhong Shan Road 2, Guangzhou, 510080, China
Jinmao Zou Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
Yaqing Li Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
Shaojie Chen Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China
Ming Gao Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Kaihong Huang Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Guoda Lian Department of Gastroenterology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.

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Truong LUF, Kleiber JC, Durot C, Brenet E, Barbe C, Hoeffel C, Bazin A, Labrousse M, Dubernard X. The study of predictive factors for the evolution of vestibular schwannomas. Eur Arch Otorhinolaryngol 2023;280:1661-1670. [PMID: 36114332 DOI: 10.1007/s00405-022-07651-w] [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: 05/15/2022] [Accepted: 09/12/2022] [Indexed: 11/30/2022]

Affiliation(s)

Le-Uyen France Truong Department of Oto-Rhino-Laryngology and Head and neck surgery of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France
Jean Charles Kleiber Department of Neurosurgery of the CHU of Reims, Hôpital Maison Blanche, 45 rue Cognacq-Jay, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Carole Durot Department of Radiology of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Esteban Brenet Department of Oto-Rhino-Laryngology and Head and neck surgery of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Coralie Barbe Research and Public Health Unit of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Christine Hoeffel Department of Radiology of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Arnaud Bazin Department of Neurosurgery of the CHU of Reims, Hôpital Maison Blanche, 45 rue Cognacq-Jay, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Marc Labrousse Department of Oto-Rhino-Laryngology and Head and neck surgery of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France
Xavier Dubernard Department of Oto-Rhino-Laryngology and Head and neck surgery of the CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France. Faculty of Medicine, Reims Champagne Ardenne University, 51100, Reims, France. Service d'ORL et Chirurgie cervico-faciale, CHU of Reims, Hôpital Robert Debré, Rue du Général Koenig, 51100, Reims, France.

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Bian Y, Zhou J, Zhu M, Yu J, Zhao H, Fang X, Liu F, Wang T, Li J, Wang L, Lu J, Shao C. Replacing secretin-enhanced MRCP with MRI radiomics model based on a fully automated pancreas segmentation for assessing pancreatic exocrine function in chronic pancreatitis. Eur Radiol 2023;33:3580-3591. [PMID: 36884086 DOI: 10.1007/s00330-023-09448-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 01/06/2023] [Accepted: 01/18/2023] [Indexed: 03/09/2023]

Abstract

OBJECTIVES

To develop and validate a radiomics nomogram based on a fully automated pancreas segmentation to assess pancreatic exocrine function. Furthermore, we aimed to compare the performance of the radiomics nomogram with the pancreatic flow output rate (PFR) and conclude on the replacement of secretin-enhanced magnetic resonance cholangiopancreatography (S-MRCP) by the radiomics nomogram for pancreatic exocrine function assessment.

METHODS

All participants underwent S-MRCP between April 2011 and December 2014 in this retrospective study. PFR was quantified using S-MRCP. Participants were divided into normal and pancreatic exocrine insufficiency (PEI) groups using the cut-off of 200 µg/L of fecal elastase-1. Two prediction models were developed including the clinical and non-enhanced T1-weighted imaging radiomics model. A multivariate logistic regression analysis was conducted to develop the prediction models. The models' performances were determined based on their discrimination, calibration, and clinical utility.

RESULTS

A total of 159 participants (mean age [Formula: see text] standard deviation, 45 years [Formula: see text] 14;119 men) included 85 normal and 74 PEI. All the participants were divided into a training set comprising 119 consecutive patients and an independent validation set comprising 40 consecutive patients. The radiomics score was an independent risk factor for PEI (odds ratio = 11.69; p < 0.001). In the validation set, the radiomics nomogram exhibited the highest performance (AUC, 0.92) in PEI prediction, whereas the clinical nomogram and PFR had AUCs of 0.79 and 0.78, respectively.

CONCLUSION

The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed pancreatic flow output rate on S-MRCP in patients with chronic pancreatitis.

KEY POINTS

• The clinical nomogram exhibited moderate performance in diagnosing pancreatic exocrine insufficiency. • The radiomics score was an independent risk factor for pancreatic exocrine insufficiency, and every point rise in the rad-score was associated with an 11.69-fold increase in pancreatic exocrine insufficiency risk. • The radiomics nomogram accurately predicted pancreatic exocrine function and outperformed the clinical model and pancreatic flow output rate quantified by secretin-enhanced magnetic resonance cholangiopancreatography on MRI in patients with chronic pancreatitis.

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Can Imaging Using Radiomics and Fat Fraction Analysis Detect Early Tissue Changes on Historical CT Scans in the Regions of the Pancreas Gland That Subsequently Develop Adenocarcinoma? Diagnostics (Basel) 2023;13:diagnostics13050941. [PMID: 36900085 PMCID: PMC10001321 DOI: 10.3390/diagnostics13050941] [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: 01/31/2023] [Revised: 02/24/2023] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open

Abstract

Despite a growing number of effective therapeutic options for patients with pancreatic adenocarcinoma, the prognosis remains dismal mostly due to the late-stage presentation and spread of the cancer to other organs. Because a genomic analysis of pancreas tissue revealed that it may take years, if not decades, for pancreatic cancer to develop, we performed radiomics and fat fraction analysis on contrast-enhanced CT (CECT) scans of patients with historical scans showing no evidence of cancer but who subsequently went on to develop pancreas cancer years later, in an attempt to identify specific imaging features of the normal pancreas that may portend the subsequent development of the cancer. In this IRB-exempt, retrospective, single institution study, CECT chest, abdomen, and pelvis (CAP) scans of 22 patients who had evaluable historical imaging data were analyzed. The images from the "healthy" pancreas were obtained between 3.8 and 13.9 years before the diagnosis of pancreas cancer was established. Afterwards, the images were used to divide and draw seven regions of interest (ROIs) around the pancreas (uncinate, head, neck-genu, body (proximal, middle, and distal) and tail). Radiomic analysis on these pancreatic ROIs consisted of first order quantitative texture analysis features such as kurtosis, skewness, and fat quantification. Of all the variables tested, fat fraction in the pancreas tail (p = 0.029) and asymmetry of the histogram frequency curve (skewness) of pancreas tissue (p = 0.038) were identified as the most important imaging signatures for subsequent cancer development. Changes in the texture of the pancreas as measured on the CECT of patients who developed pancreas cancer years later could be identified, confirming the utility of radiomics-based imaging as a potential predictor of oncologic outcomes. Such findings may be potentially useful in the future to screen patients for pancreatic cancer, thereby helping detect pancreas cancer at an early stage and improving survival.

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Jamshidi N, Senthilvelan J, Dawson DW, Donahue TR, Kuo MD. Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma. BMC Cancer 2023;23:189. [PMID: 36843111 PMCID: PMC9969670 DOI: 10.1186/s12885-023-10658-z] [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: 08/30/2022] [Accepted: 02/17/2023] [Indexed: 02/28/2023] Open

Abstract

BACKGROUND

Pancreatic adenocarcinoma (PDAC) persists as a malignancy with high morbidity and mortality that can benefit from new means to characterize and detect these tumors, such as radiogenomics. In order to address this gap in the literature, constructed a transcriptomic-CT radiogenomic (RG) map for PDAC.

METHODS

In this Institutional Review Board approved study, a cohort of subjects (n = 50) with gene expression profile data paired with histopathologically confirmed resectable or borderline resectable PDAC were identified. Studies with pre-operative contrast-enhanced CT images were independently assessed for a set of 88 predefined imaging features. Microarray gene expression profiling was then carried out on the histopathologically confirmed pancreatic adenocarcinomas and gene networks were constructed using Weighted Gene Correlation Network Analysis (WCGNA) (n = 37). Data were analyzed with bioinformatics analyses, multivariate regression-based methods, and Kaplan-Meier survival analyses.

RESULTS

Survival analyses identified multiple features of interest that were significantly associated with overall survival, including Tumor Height (P = 0.014), Tumor Contour (P = 0.033), Tumor-stroma Interface (P = 0.014), and the Tumor Enhancement Ratio (P = 0.047). Gene networks for these imaging features were then constructed using WCGNA and further annotated according to the Gene Ontology (GO) annotation framework for a biologically coherent interpretation of the imaging trait-associated gene networks, ultimately resulting in a PDAC RG CT-transcriptome map composed of 3 stage-independent imaging traits enriched in metabolic processes, telomerase activity, and podosome assembly (P < 0.05).

CONCLUSIONS

A CT-transcriptomic RG map for PDAC composed of semantic and quantitative traits with associated biology processes predictive of overall survival, was constructed, that serves as a reference for further mechanistic studies for non-invasive phenotyping of pancreatic tumors.

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Radiomics analysis of contrast-enhanced T1W MRI: predicting the recurrence of acute pancreatitis. Sci Rep 2023;13:2762. [PMID: 36797285 PMCID: PMC9935887 DOI: 10.1038/s41598-022-13650-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Accepted: 05/26/2022] [Indexed: 02/18/2023] Open

Tikhonova VS, Karmazanovsky GG, Kondratyev EV, Gruzdev IS, Mikhaylyuk KA, Sinelnikov MY, Revishvili AS. Radiomics model-based algorithm for preoperative prediction of pancreatic ductal adenocarcinoma grade. Eur Radiol 2023;33:1152-1161. [PMID: 35986774 DOI: 10.1007/s00330-022-09046-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 05/24/2022] [Accepted: 07/13/2022] [Indexed: 02/03/2023]

Abstract

OBJECTIVE

To develop diagnostic radiomic model-based algorithm for pancreatic ductal adenocarcinoma (PDAC) grade prediction.

METHODS

Ninety-one patients with histologically confirmed PDAC and preoperative CT were divided into subgroups based on tumor grade. Two histology-blinded radiologists independently segmented lesions for quantitative texture analysis in all contrast enhancement phases. The ratio of densities of PDAC and unchanged pancreatic tissue, and relative tumor enhancement (RTE) in arterial, portal venous, and delayed phases of the examination were calculated. Principal component analysis was used for multivariate predictor analysis. The selection of predictors in the binary logistic model was carried out in 2 stages: (1) using one-factor logistic models (selection criterion was p < 0.1); (2) using regularization (LASSO regression after standardization of variables). Predictors were included in proportional odds models without interactions.

RESULTS

There were significant differences in 4, 16, and 8 texture features out of 62 for the arterial, portal venous, and delayed phases of the study, respectively (p < 0.1). After selection, the final diagnostic model included such radiomics features as DISCRETIZED HU standard, DISCRETIZED HUQ3, GLCM Correlation, GLZLM LZLGE for the portal venous phase of the contrast enhancement, and CONVENTIONAL_HUQ3 for the delayed phase of CT study. On its basis, a diagnostic model was built, showing AUC for grade ≥ 2 of 0.75 and AUC for grade 3 of 0.66.

CONCLUSION

Radiomics features vary in PDAC of different grades and increase the accuracy of CT in preoperative diagnosis. We have developed a diagnostic model, including texture features, which can be used to predict the grade of PDAC.

KEY POINTS

• A diagnostic algorithm based on CT texture features for preoperative PDAC grade prediction was developed. • The assumption that the scanning protocol can influence the results of texture analysis was confirmed and assessed. • Our results show that tumor differentiation grade can be assessed with sufficient diagnostic accuracy using CT texture analysis presented in this study.

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Lu J, Jiang N, Zhang Y, Li D. A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front Oncol 2023;13:979437. [PMID: 36937433 PMCID: PMC10014827 DOI: 10.3389/fonc.2023.979437] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/20/2023] [Indexed: 03/05/2023] Open

Abstract

Objectives

The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma.

Methods

96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability.

Results

A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually.

Conclusions

The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.

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Wang F, Zhao Y, Xu J, Shao S, Yu D. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients. Front Oncol 2022;12:1037672. [PMID: 36518321 PMCID: PMC9742428 DOI: 10.3389/fonc.2022.1037672] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2023] Open

Abstract

PURPOSE

To develop and externally validate a prognosis nomogram based on contrast-enhanced computed tomography (CECT) combined clinical for preoperative prognosis prediction of patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

184 patients from Center A with histopathologically confirmed PDAC who underwent CECT were included and allocated to training cohort (n=111) and internal validation cohort (n=28). The radiomic score (Rad - score) for predicting overall survival (OS) was constructed by using the least absolute shrinkage and selection operator (LASSO). Univariate and multivariable Cox regression analysis was used to construct clinic-pathologic features. Finally, a radiomics nomogram incorporating the Rad - score and clinical features was established. External validation was performed using Center B dataset (n = 45). The validation of nomogram was evaluated by calibration curve, Harrell's concordance index (C-index) and decision curve analysis (DCA). The Kaplan-Meier (K-M) method was used for OS analysis.

RESULTS

Univariate and multivariate analysis indicated that Rad - score, preoperative CA 19-9 and postoperative American Joint Committee on Cancer (AJCC) TNM stage were significant prognostic factors. The nomogram based on Rad - score and preoperative CA19-9 was found to exhibit excellent prediction ability: in the training cohort, C-index was superior to that of the preoperative CA19-9 (0.713 vs 0.616, P< 0.001) and AJCC TNM stage (0.713 vs 0.614, P< 0.001); the C-index was also had good performance in the validation cohort compared with CA19-9 (internal validation cohort: 0.694 vs 0.555, P< 0.001; external validation cohort: 0.684 vs 0.607, P< 0.001) and AJCC TNM stage (internal validation cohort: 0.694 vs 0.563, P< 0.001; external validation cohort: 0.684 vs 0.596, P< 0.001). The calibration plot and DCA showed excellent predictive accuracy in the validation cohort.

CONCLUSION

We established a well-designed nomogram to accurately predict OS of PDAC preoperatively. The nomogram showed a satisfactory prediction effect and was worthy of further evaluation in the future.

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Yang J, Liu Y, Liu S. Comment on "Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer". World J Gastroenterol 2022;28:6310-6313. [PMID: 36504551 PMCID: PMC9730437 DOI: 10.3748/wjg.v28.i44.6310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/26/2022] [Accepted: 11/16/2022] [Indexed: 02/06/2023] Open

Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis. Clin Transl Gastroenterol 2022;14:e00548. [PMID: 36434803 PMCID: PMC9875961 DOI: 10.14309/ctg.0000000000000548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 10/18/2022] [Indexed: 11/27/2022] Open

Abstract

INTRODUCTION

Pancreatic cancer is the third leading cause of cancer deaths among men and women in the United States. We aimed to detect early changes on computed tomography (CT) images associated with pancreatic ductal adenocarcinoma (PDAC) based on quantitative imaging features (QIFs) for patients with and without chronic pancreatitis (CP).

METHODS

Adults 18 years and older diagnosed with PDAC in 2008-2018 were identified. Their CT scans 3 months-3 years before the diagnosis date were matched to up to 2 scans of controls. The pancreas was automatically segmented using a previously developed algorithm. One hundred eleven QIFs were extracted. The data set was randomly split for training/validation. Neighborhood and principal component analyses were applied to select the most important features. A conditional support vector machine was used to develop prediction algorithms separately for patients with and without CP. The computer labels were compared with manually reviewed CT images 2-3 years before the index date in 19 cases and 19 controls.

RESULTS

Two hundred twenty-seven of 554 scans of non-CP cancer cases/controls and 70 of 140 scans of CP cancer cases/controls were included (average age 71 and 68 years, 51% and 44% females for non-CP patients and patients with CP, respectively). The QIF-based algorithms varied based on CP status. For non-CP patients, accuracy measures were 94%-95% and area under the curve (AUC) measures were 0.98-0.99. Sensitivity, specificity, positive predictive value, and negative predictive value were in the ranges of 88%-91%, 96%-98%, 91%-95%, and 94%-96%, respectively. QIFs on CT examinations within 2-3 years before the index date also had very high predictive accuracy (accuracy 95%-98%; AUC 0.99-1.00). The QIF-based algorithm outperformed manual rereview of images for determination of PDAC risk. For patients with CP, the algorithms predicted PDAC perfectly (accuracy 100% and AUC 1.00).

DISCUSSION

QIFs can accurately predict PDAC for both non-CP patients and patients with CP on CT imaging and represent promising biomarkers for early detection of pancreatic cancer.

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Kаrmаzаnovsky GG, Kondratyev EV, Gruzdev IS, Tikhonova VS, Shantarevich MY, Zamyatina KA, Stashkiv VI, Revishvili AS. Modern Radiation Diagnostics and Intelligent Personalized Technologies in Hepatopancreatology. ANNALS OF THE RUSSIAN ACADEMY OF MEDICAL SCIENCES 2022;77:245-253. [DOI: 10.15690/vramn2053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]

Abstract Timely instrumental diagnosis of diseases of the hepatopancreatoduodenal region, especially of an oncological nature, is the key to successful treatment, improving prognosis and improving the quality of life of patients. At the moment, the possibilities of radiation diagnostics make it possible to identify and evaluate the nature of the blood supply to the neoplasm, its prevalence, cellularity, and in the case of MRI studies with hepatospecific contrast agents, also evaluate the functional activity of liver cells. Nevertheless, the steady development of methods for treating cancer patients, in particular, chemotherapy, and a personalized approach to the choice of patient management tactics require a detailed assessment of the morphological types of certain neoplasms. The need for dynamic monitoring of the results of treatment, monitoring of accidentally detected, potentially malignant neoplasms, and the development of screening programs determine the steady increase in the number of CT and MR examinations performed annually in the world and in our country. These factors have led to the application of texture analysis or radiomics and machine learning algorithms. At the same time, such techniques as radiography, ultrasound, CT and MRI with extracellular and tissue-specific contrast enhancement, and MRI-DWI do not lose their significance. The ongoing research allows the Federal State Budgetary Institution National Medical Research Center of Surgery named after A.V. Vishnevsky of the Ministry of Health of Russia to implement the concept of preoperative non-invasive diagnosis and differential diagnosis of surgical and oncological diseases of the hepatopancreatoduodenal region and apply the knowledge gained in planning surgical treatment. Implementation of the problem of post-processor data processing of radiation diagnostics of surgical and oncological diseases of the hepatopancreatoduodenal region using radiomics and AI technologies is important and extremely relevant for modern medicine. Collapse

Mukherjee S, Patra A, Khasawneh H, Korfiatis P, Rajamohan N, Suman G, Majumder S, Panda A, Johnson MP, Larson NB, Wright DE, Kline TL, Fletcher JG, Chari ST, Goenka AH. Radiomics-based Machine-learning Models Can Detect Pancreatic Cancer on Prediagnostic Computed Tomography Scans at a Substantial Lead Time Before Clinical Diagnosis. Gastroenterology 2022;163:1435-1446.e3. [PMID: 35788343 DOI: 10.1053/j.gastro.2022.06.066] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 06/20/2022] [Accepted: 06/22/2022] [Indexed: 02/01/2023]

Abstract

BACKGROUND & AIMS

Our purpose was to detect pancreatic ductal adenocarcinoma (PDAC) at the prediagnostic stage (3-36 months before clinical diagnosis) using radiomics-based machine-learning (ML) models, and to compare performance against radiologists in a case-control study.

METHODS

Volumetric pancreas segmentation was performed on prediagnostic computed tomography scans (CTs) (median interval between CT and PDAC diagnosis: 398 days) of 155 patients and an age-matched cohort of 265 subjects with normal pancreas. A total of 88 first-order and gray-level radiomic features were extracted and 34 features were selected through the least absolute shrinkage and selection operator-based feature selection method. The dataset was randomly divided into training (292 CTs: 110 prediagnostic and 182 controls) and test subsets (128 CTs: 45 prediagnostic and 83 controls). Four ML classifiers, k-nearest neighbor (KNN), support vector machine (SVM), random forest (RM), and extreme gradient boosting (XGBoost), were evaluated. Specificity of model with highest accuracy was further validated on an independent internal dataset (n = 176) and the public National Institutes of Health dataset (n = 80). Two radiologists (R4 and R5) independently evaluated the pancreas on a 5-point diagnostic scale.

RESULTS

Median (range) time between prediagnostic CTs of the test subset and PDAC diagnosis was 386 (97-1092) days. SVM had the highest sensitivity (mean; 95% confidence interval) (95.5; 85.5-100.0), specificity (90.3; 84.3-91.5), F1-score (89.5; 82.3-91.7), area under the curve (AUC) (0.98; 0.94-0.98), and accuracy (92.2%; 86.7-93.7) for classification of CTs into prediagnostic versus normal. All 3 other ML models, KNN, RF, and XGBoost, had comparable AUCs (0.95, 0.95, and 0.96, respectively). The high specificity of SVM was generalizable to both the independent internal (92.6%) and the National Institutes of Health dataset (96.2%). In contrast, interreader radiologist agreement was only fair (Cohen's kappa 0.3) and their mean AUC (0.66; 0.46-0.86) was lower than each of the 4 ML models (AUCs: 0.95-0.98) (P < .001). Radiologists also recorded false positive indirect findings of PDAC in control subjects (n = 83) (7% R4, 18% R5).

CONCLUSIONS

Radiomics-based ML models can detect PDAC from normal pancreas when it is beyond human interrogation capability at a substantial lead time before clinical diagnosis. Prospective validation and integration of such models with complementary fluid-based biomarkers has the potential for PDAC detection at a stage when surgical cure is a possibility.

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CT perfusion as a potential biomarker for pancreatic ductal adenocarcinoma during routine staging and restaging. ABDOMINAL RADIOLOGY (NEW YORK) 2022;47:3770-3781. [PMID: 35972550 DOI: 10.1007/s00261-022-03638-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023]

Abstract

PURPOSE

To evaluate the significance of CT perfusion parameters predicting response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

Seventy patients with PDAC prospectively had CT perfusion acquisition incorporated into baseline multiphase staging CT. Twenty-eight who were naïve to therapy were retained for further investigation. Perfusion was performed 5-42.5 s after contrast, followed by parenchymal and portal venous phases. Blood flow (BF), blood volume (BV), and permeability surface area product (PS) were calculated using deconvolution algorithms. Patients were categorized as responders or non-responders per RECIST 1.1. Perfusion variables with AUC ≥ 0.70 in differentiating responders from non-responders were retained. Logistic regression was used to assess associations between baseline perfusion variables and response.

RESULTS

18 of 28 patients showed favorable response to therapy. Baseline heterogeneity variables in tumor max ROI were higher in non-responders than responders [median BF coefficient of variation (CV) 0.91 vs. 0.51 respectively, odds ratio (OR) 6.8 per one standard deviation (1-SD) increase, P = 0.047; median PS CV 1.6 vs. 0.68, OR 3.9 per 1-SD increase, P = 0.047; and median BV CV 0.75 vs. 0.54, OR = 4.0 per 1-SD increase, P = 0.047]. Baseline BV mean in tumor center was lower in non-responders than responders (median BV mean: 0.74 vs. 2.9 ml/100 g respectively, OR 0.28 per 1-SD increase, P = 0.047).

CONCLUSION

For patients with PDAC receiving neoadjuvant therapy, lower and more heterogeneous perfusion parameters correlated with an unfavorable response to therapy. Such quantitative information can be acquired utilizing a comprehensive protocol interleaving perfusion CT acquisition with standard of care multiphase CT scans using a single contrast injection, which could be used to identify surgical candidates and predict outcome.

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Barat M, Marchese U, Pellat A, Dohan A, Coriat R, Hoeffel C, Fishman EK, Cassinotto C, Chu L, Soyer P. Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances. Can Assoc Radiol J 2022;74:351-361. [PMID: 36065572 DOI: 10.1177/08465371221124927] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open

Liver metastases in pancreatic ductal adenocarcinoma: a predictive model based on CT texture analysis. Radiol Med 2022;127:1079-1084. [PMID: 36057929 DOI: 10.1007/s11547-022-01548-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 08/18/2022] [Indexed: 10/14/2022]

Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022;95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open

Abstract

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.

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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022;47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]

Keyl J, Kasper S, Wiesweg M, Götze J, Schönrock M, Sinn M, Berger A, Nasca E, Kostbade K, Schumacher B, Markus P, Albers D, Treckmann J, Schmid KW, Schildhaus HU, Siveke JT, Schuler M, Kleesiek J. Multimodal survival prediction in advanced pancreatic cancer using machine learning. ESMO Open 2022;7:100555. [PMID: 35988455 PMCID: PMC9588888 DOI: 10.1016/j.esmoop.2022.100555] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 11/23/2022] Open

Affiliation(s)

J Keyl Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany.
S Kasper Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
M Wiesweg Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
J Götze Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
M Schönrock Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
M Sinn Department of Internal Medicine II, Oncology, Hematology, BMT and Pneumology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
A Berger Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany
E Nasca Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany
K Kostbade Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
B Schumacher Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
P Markus Department of General Surgery and Traumatology, Elisabeth Hospital Essen, Essen, Germany
D Albers Department of Gastroenterology, Elisabeth Hospital Essen, Essen, Germany
J Treckmann Department of General, Visceral and Transplant Surgery, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
K W Schmid Medical Faculty, University of Duisburg-Essen, Essen, Germany; Institute of Pathology, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
H-U Schildhaus Medical Faculty, University of Duisburg-Essen, Essen, Germany; Institute of Pathology, West German Cancer Center, University Hospital Essen (AöR), Essen, Germany
J T Siveke Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany; Bridge Institute of Experimental Tumor Therapy (BIT), West German Cancer Center, University Hospital Essen (AöR), Essen, Germany; Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK) Partner site Essen, German Cancer Research Center (DKFZ), Heidelberg, Germany
M Schuler Department of Medical Oncology, West German Cancer Center, University Hospital Essen (AöR), University of Duisburg-Essen, Essen, Germany; German Cancer Consortium (DKTK), Partner site University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany
J Kleesiek Institute for AI in Medicine (IKIM), University Hospital Essen (AöR), Essen, Germany; Medical Faculty, University of Duisburg-Essen, Essen, Germany

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de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2022;21:356-361. [PMID: 34961674 DOI: 10.1016/j.hbpd.2021.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 02/05/2023]

A systematic review of prognosis predictive role of radiomics in pancreatic cancer: heterogeneity markers or statistical tricks? Eur Radiol 2022;32:8443-8452. [PMID: 35904618 DOI: 10.1007/s00330-022-08922-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Revised: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 12/14/2022]

Abstract

OBJECTIVES

We aimed to systematically evaluate the prognostic prediction accuracy of radiomics features extracted from pre-treatment imaging in patients with pancreatic ductal adenocarcinoma (PDAC).

METHODS

Radiomics literature on overall survival (OS) prediction of PDAC were all included in this systematic review. A further meta-analysis was performed on the effect size of first-order entropy. Methodological quality and risk of bias of the included studies were assessed by the radiomics quality score (RQS) and prediction model risk of bias assessment tool (PROBAST).

RESULTS

Twenty-three studies were finally identified in this review. Two (8.7%) studies compared prognosis prediction ability between radiomics model and TNM staging model by C-index, and both showed a better performance of the radiomics. Twenty-one (91.3%) studies reported significant predictive values of radiomics features. Nine (39.1%) studies were included in the meta-analysis, and it showed a significant correlation between first-order entropy and OS (HR 1.66, 95%CI 1.18-2.34). RQS assessment revealed validation was only performed in 5 (21.7%) studies on internal datasets and 2 (8.7%) studies on external datasets. PROBAST showed that 22 (95.7%) studies have a high risk of bias in participants because of the retrospective study design.

CONCLUSION

First-order entropy was significantly associated with OS and might improve the accuracy of PDAC prognosis prediction. Existing studies were poorly validated, and it should be noted in future studies. Modification of PROBAST for radiomics studies is necessary since the strict requirements of prospective study design may not be applicable to the demand for a large sample size in the model construction stage.

KEY POINTS

• Radiomics based on the primary lesion holds great potential for prognosis prediction. First-order entropy was significantly associated with the overall survival of PDAC and might improve the accuracy of current PDAC prognosis prediction. • We strongly recommend that at least an internal validation should be conducted in any radiomics study. Attention should be paid to the complex relationships between radiomics features. • Due to the close relationship between radiomics and big data, the strict requirement of prospective study design in PROABST may not be appropriate for radiomics studies. A balance between study types and sample sizes for radiomics studies needs to be found in the model construction stage.

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Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma. Acad Radiol 2022;30:680-688. [PMID: 35906151 DOI: 10.1016/j.acra.2022.05.019] [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: 03/18/2022] [Revised: 05/24/2022] [Accepted: 05/29/2022] [Indexed: 11/21/2022]

Abstract

OBJECTIVE

To develop and validate an effective model for identifying patients with postoperative local disease recurrence of pancreatic ductal adenocarcinoma (PDAC).

METHODS

A total of 153 patients who had undergone surgical resection of PDAC with regular postoperative follow-up were consecutively enrolled and randomly divided into training (n = 108) and validation (n = 45) cohorts. The postoperative soft-tissue biopsy results or clinical follow-up results served as the reference diagnostic criteria. Radiomics analysis of the postoperative soft-tissue was performed on a commercially available prototype software using portal vein phase image. Three models were built to characterize postoperative soft tissue: computed tomography (CT)-based radiomics, clinicoradiological, and their combination. The area under the receiver operating characteristic curves (AUC) was used to evaluate the differential diagnostic performance. A nomogram was used to select the final model with best performance. One radiologist's diagnostic choices that were made with and without the nomogram's assistance were evaluated.

RESULTS

A seven-feature-combined radiomics signature was constructed as a predictor of postoperative local recurrence. The nomogram model combining the radiomics signature with postoperative CA 19-9 elevation showed the best performance (training cohort, AUC = 0.791 [95%CI: 0.707, 0.876]; validation cohort, AUC = 0.742 [95%CI: 0.590, 0.894]). In the validation cohort, the AUC for differential diagnosis was significantly improved for the combined model relative to that for postoperative CA 19-9 elevation (AUC = 0.742 vs. 0.533, p < 0.001). The calibration curve and decision curve analysis demonstrated the clinical usefulness of the proposed nomogram. The diagnostic performance of the radiologist was not significantly improve by using the proposed nomogram (AUC = 0.742 vs. 0.670, p = 0.17).

CONCLUSION

The combined model using CT radiomic features and CA 19-9 elevation effectively characterized postoperative soft tissue and potentially may improve treatment strategies and facilitate personalized treatment for PDAC after surgical resection.

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Zhou L, Feng F, Yang Y, Zheng X, Yang Y. Prognostic predictors of non-small cell lung cancer treated with curative resection: the role of preoperative CT texture features, clinical features, and laboratory parameters. Clin Radiol 2022;77:e765-e770. [PMID: 35843728 DOI: 10.1016/j.crad.2022.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 06/11/2022] [Accepted: 06/15/2022] [Indexed: 11/03/2022]

Zhang Y, Zhuang Y, Ge Y, Wu PY, Zhao J, Wang H, Song B. MRI whole-lesion texture analysis on ADC maps for the prognostic assessment of ischemic stroke. BMC Med Imaging 2022;22:115. [PMID: 35778678 PMCID: PMC9250246 DOI: 10.1186/s12880-022-00845-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/23/2022] [Indexed: 11/28/2022] Open

Abstract

Background

This study aims is to explore whether it is feasible to use magnetic resonance texture analysis (MRTA) in order to distinguish favorable from unfavorable function outcomes and determine the prognostic factors associated with favorable outcomes of stroke.

Methods

The retrospective study included 103 consecutive patients who confirmed unilateral anterior circulation subacute ischemic stroke by computed tomography angiography between January 2018 and September 2019. Patients were divided into favorable outcome (modified Rankin scale, mRS ≤ 2) and unfavorable outcome (mRS > 2) groups according to mRS scores at day 90. Two radiologists manually segmented the infarction lesions based on diffusion-weighted imaging and transferred the images to corresponding apparent diffusion coefficient (ADC) maps in order to extract texture features. The prediction models including clinical characteristics and texture features were built using multiple logistic regression. A univariate analysis was conducted to assess the performance of the mean ADC value of the infarction lesion. A Delong’s test was used to compare the predictive performance of models through the receiver operating characteristic curve.

Results

The mean ADC performance was moderate [AUC = 0.60, 95% confidence interval (CI) 0.49–0.71]. The texture feature model of the ADC map (tADC), contained seven texture features, and presented good prediction performance (AUC = 0.83, 95%CI 0.75–0.91). The energy obtained after wavelet transform, and the kurtosis and skewness obtained after Laplacian of Gaussian transformation were identified as independent prognostic factors for the favorable stroke outcomes. In addition, the combination of the tADC model and clinical characteristics (hypertension, diabetes mellitus, smoking, and atrial fibrillation) exhibited a subtly better performance (AUC = 0.86, 95%CI 0.79–0.93; P > 0.05, Delong’s).

Conclusion

The models based on MRTA on ADC maps are useful to evaluate the clinical function outcomes in patients with unilateral anterior circulation ischemic stroke. Energy obtained after wavelet transform, kurtosis obtained after Laplacian of Gaussian transform, and skewness obtained after Laplacian of Gaussian transform were identified as independent prognostic factors for favorable stroke outcomes.

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Gao JF, Pan Y, Lin XC, Lu FC, Qiu DS, Liu JJ, Huang HG. Prognostic value of preoperative enhanced computed tomography as a quantitative imaging biomarker in pancreatic cancer. World J Gastroenterol 2022;28:2468-2481. [PMID: 35979266 PMCID: PMC9258279 DOI: 10.3748/wjg.v28.i22.2468] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/31/2021] [Accepted: 05/17/2022] [Indexed: 02/06/2023] Open

Abstract

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with high mortality and short survival time. Computed tomography (CT) plays an important role in the diagnosis, staging and treatment of pancreatic tumour. Pancreatic cancer generally shows a low enhancement pattern compared with normal pancreatic tissue.

AIM

To analyse whether preoperative enhanced CT could be used to predict postoperative overall survival in patients with PDAC.

METHODS

Sixty-seven patients with PDAC undergoing pancreatic resection were enrolled retrospectively. All patients underwent preoperative unenhanced and enhanced CT examination, the CT values of which were measured. The ratio of the preoperative CT value increase from the nonenhancement phase to the portal venous phase between pancreatic tumour and normal pancreatic tissue was calculated. The cut-off value of ratios was obtained by the receiver operating characteristic (ROC) curve of the tumour relative enhancement ratio (TRER), according to which patients were divided into low- and high-enhancement groups. Univariate and multivariate analyses were performed using Cox regression based on TRER grouping. Finally, the correlation between TRER and clinicopathological characteristics was analysed.

RESULTS

The area under the curve of the ROC curve was 0.768 (P < 0.05), and the cut-off value of the ROC curve was calculated as 0.7. TRER ≤ 0.7 was defined as the low-enhancement group, and TRER > 0.7 was defined as the high-enhancement group. According to the TRER grouping, the Kaplan-Meier survival curve analysis results showed that the median survival (10.0 mo) with TRER ≤ 0.7 was significantly shorter than that (22.0 mo) with TRER > 0.7 (P < 0.05). In the univariate and multivariate analyses, the prognosis of patients with TRER ≤ 0.7 was significantly worse than that of patients with TRER > 0.7 (P < 0.05). Our results demonstrated that patients in the low TRER group were more likely to have higher American Joint Committee on Cancer stage, tumour stage and lymph node stage (all P < 0.05), and TRER was significantly negatively correlated with tumour size (P < 0.05).

CONCLUSION

TRER ≤ 0.7 in patients with PDAC may represent a tumour with higher clinical stage and result in a shorter overall survival.

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Quantitative MRI of Pancreatic Cystic Lesions: A New Diagnostic Approach. Healthcare (Basel) 2022;10:healthcare10061039. [PMID: 35742090 PMCID: PMC9222599 DOI: 10.3390/healthcare10061039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 05/30/2022] [Accepted: 05/31/2022] [Indexed: 02/01/2023] Open

Bekci T, Cakir IM, Aslan S. Differentiation of affected and nonaffected ovaries in ovarian torsion with magnetic resonance imaging texture analysis. REVISTA DA ASSOCIACAO MEDICA BRASILEIRA (1992) 2022;68:641-646. [PMID: 35584489 DOI: 10.1590/1806-9282.20211369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 01/03/2022] [Indexed: 11/22/2022]

Yang Q, Mao Y, Xie H, Qin T, Mai Z, Cai Q, Wen H, Li Y, Zhang R, Liu L. Identifying Outcomes of Patients With Advanced Pancreatic Adenocarcinoma and RECIST Stable Disease Using Radiomics Analysis. JCO Precis Oncol 2022;6:e2100362. [PMID: 35319966 PMCID: PMC8966975 DOI: 10.1200/po.21.00362] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open

Rossi G, Altabella L, Simoni N, Benetti G, Rossi R, Venezia M, Paiella S, Malleo G, Salvia R, Guariglia S, Bassi C, Cavedon C, Mazzarotto R. Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy. World J Gastrointest Oncol 2022;14:703-715. [PMID: 35321278 PMCID: PMC8919018 DOI: 10.4251/wjgo.v14.i3.703] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 08/06/2021] [Accepted: 02/11/2022] [Indexed: 02/06/2023] Open

Abstract

BACKGROUND

Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer (LAPC). However, the diagnostic performance of computed tomography (CT) imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation. In this context, radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional, unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue, potentially serving in the therapeutic decision-making process.

AIM

To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy.

METHODS

Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed. One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume. Both extracted features and clinical data contribute to create and validate the predictive model of resectability status. Patients were repeatedly divided into training and validation sets. The discriminating performance of each model, obtained applying a LASSO regression analysis, was assessed with the area under the receiver operating characteristic curve (AUC). The validated model was applied to the entire dataset to obtain the most significant features.

RESULTS

Seventy-one patients were included in the analysis. Median age was 65 years and 57.8% of patients were male. All patients underwent induction chemotherapy followed by ablative radiotherapy, and 19 (26.8%) ultimately received surgical resection. After the first step of variable selections, a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862 (95%CI: 0.792-0.921) and 0.853 (95%CI: 0.706-0.960), respectively. The validated model was applied to the entire dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944 (95%CI: 0.892-0.996).

CONCLUSION

The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy, potentially integrating clinical and morphological parameters in predicting surgical resection.

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Radiofrequency ablation of hepatocellular carcinoma: CT texture analysis of the ablated area to predict local recurrence. Eur J Radiol 2022;150:110250. [DOI: 10.1016/j.ejrad.2022.110250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 02/25/2022] [Accepted: 03/07/2022] [Indexed: 11/22/2022]

Zhang X, Zhang Y, Zhang G, Qiu X, Tan W, Yin X, Liao L. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol 2022;12:773840. [PMID: 35251962 PMCID: PMC8891653 DOI: 10.3389/fonc.2022.773840] [Citation(s) in RCA: 58] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022] Open

Abstract

The high-throughput extraction of quantitative imaging features from medical images for the purpose of radiomic analysis, i.e., radiomics in a broad sense, is a rapidly developing and emerging research field that has been attracting increasing interest, particularly in multimodality and multi-omics studies. In this context, the quantitative analysis of multidimensional data plays an essential role in assessing the spatio-temporal characteristics of different tissues and organs and their microenvironment. Herein, recent developments in this method, including manually defined features, data acquisition and preprocessing, lesion segmentation, feature extraction, feature selection and dimension reduction, statistical analysis, and model construction, are reviewed. In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation, and inadequate feature criteria, and multicenter validation. Furthermore, a summary of the current state-of-the-art applications of this technology in disease diagnosis, treatment response, and prognosis prediction from the perspective of radiology images, multimodality images, histopathology images, and three-dimensional dose distribution data, particularly in oncology, is presented. The potential and value of radiomics in diagnostic and therapeutic strategies are also further analyzed, and for the first time, the advances and challenges associated with dosiomics in radiotherapy are summarized, highlighting the latest progress in radiomics. Finally, a robust framework for radiomic analysis is presented and challenges and recommendations for future development are discussed, including but not limited to the factors that affect model stability (medical big data and multitype data and expert knowledge in medical), limitations of data-driven processes (reproducibility and interpretability of studies, different treatment alternatives for various institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).

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Ma X, Wang YR, Zhuo LY, Yin XP, Ren JL, Li CY, Xing LH, Zheng TT. Retrospective Analysis of the Value of Enhanced CT Radiomics Analysis in the Differential Diagnosis Between Pancreatic Cancer and Chronic Pancreatitis. Int J Gen Med 2022;15:233-241. [PMID: 35023961 PMCID: PMC8747707 DOI: 10.2147/ijgm.s337455] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 12/13/2021] [Indexed: 12/12/2022] Open

Chen HY, Deng XY, Pan Y, Chen JY, Liu YY, Chen WJ, Yang H, Zheng Y, Yang YB, Liu C, Shao GL, Yu RS. Pancreatic Serous Cystic Neoplasms and Mucinous Cystic Neoplasms: Differential Diagnosis by Combining Imaging Features and Enhanced CT Texture Analysis. Front Oncol 2022;11:745001. [PMID: 35004272 PMCID: PMC8733460 DOI: 10.3389/fonc.2021.745001] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 11/29/2021] [Indexed: 12/25/2022] Open

Affiliation(s)

Hai-Yan Chen Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Xue-Ying Deng Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Yao Pan Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Jie-Yu Chen Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Yun-Ying Liu Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Department of Pathology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
Wu-Jie Chen Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Hong Yang Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Yao Zheng Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Yong-Bo Yang Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China
Cheng Liu Research Institute of Artificial Intelligence in Healthcare, Hangzhou YITU Healthcare Technology Co. Ltd., Hangzhou, China
Guo-Liang Shao Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China.,Institue of Cancer and Basic Medicine (ICBM), Chinese Academy of Sciences, Hangzhou, China.,Clinical Research Center of Hepatobiliary and Pancreatic Diseases of Zhejiang Province, Hangzhou, China
Ri-Sheng Yu Department of Radiology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

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Non-contrast-enhanced CT texture analysis of primary and metastatic pancreatic ductal adenocarcinomas: value in assessment of histopathological grade and differences between primary and metastatic lesions. Abdom Radiol (NY) 2022;47:4151-4159. [PMID: 36104481 PMCID: PMC9626421 DOI: 10.1007/s00261-022-03646-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 08/02/2022] [Accepted: 08/03/2022] [Indexed: 01/18/2023]

Park HJ, Qin L, Bakouny Z, Krajewski KM, Van Allen EM, Choueiri TK, Shinagare AB. OUP accepted manuscript. Oncologist 2022;27:389-397. [PMID: 35348767 PMCID: PMC9074990 DOI: 10.1093/oncolo/oyac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 01/07/2022] [Indexed: 11/15/2022] Open

Li X, Wan Y, Lou J, Xu L, Shi A, Yang L, Fan Y, Yang J, Huang J, Wu Y, Niu T. Preoperative recurrence prediction in pancreatic ductal adenocarcinoma after radical resection using radiomics of diagnostic computed tomography. EClinicalMedicine 2022;43:101215. [PMID: 34927034 PMCID: PMC8649647 DOI: 10.1016/j.eclinm.2021.101215] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Revised: 11/05/2021] [Accepted: 11/11/2021] [Indexed: 12/27/2022] Open

Abstract

BACKGROUND

The high recurrence rate after radical resection of pancreatic ductal adenocarcinoma (PDAC) leads to its poor prognosis. We aimed to develop a model to preoperatively predict the risk of recurrence based on computed tomography (CT) radiomics and multiple clinical parameters.

METHODS

Datasets were retrospectively collected and analysed of 220 PDAC patients who underwent contrast-enhanced computed tomography (CE-CT) and received radical resection at 3 institutions in China between 2013 and 2017, with 153 from one institution as a training set, the remaining 67 as a validation set. For each patient, CT radiomics features were extracted from intratumoral and peritumoral regions to establish intratumoral, peritumoral and combined radiomics models using artificial neural network (ANN) algorithm. By incorporating clinical factors, radiomics-clinical nomograms were finally built by multivariable logistic regression analysis to predict 1- and 2-year recurrence risk.

FINDINGS

The developed radiomics model integrating intratumoral and peritumoral radiomics features was superior to the conventionally constructed model merely using intratumoral radiomics features. Further, radiomics-clinical nomograms outperformed other models in predicting 1-year recurrence with an area under the receiver operating characteristic curve (AUROC) of 0.916 (95%CI, 0.860-0.955) in the training set and 0.764 (95%CI, 0.644-0.859) in the validation set, and 2-year recurrence with an AUROC of 0.872 (95%CI: 0.809-0.921) in the training set and 0.773 (95%CI, 0.654-0.866) in the validation set.

INTERPRETATION

This study has developed and externally validated a radiomics-clinical nomogram integrating intra- and peritumoral CT radiomics signature as well as clinical factors to predict the recurrence risk of PDAC after radical resection, which will facilitate optimized and individualized treatment strategies.

FUNDING

This work was supported by the National Key R&D Program of China [grant number: 2018YFE0114800], the General Program of National Natural Science Foundation of China [grant number: 81772562, 2017; 81871351, 2018], the Fundamental Research Funds for the Central Universities [grant number: 2021FZZX005-08], and Zhejiang Provincial Key Projects of Technology Research [grant number: WKJ-ZJ-2033].

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Affiliation(s)

Xiawei Li Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
Yidong Wan Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
Jianyao Lou Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
Lei Xu Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
Aiguang Shi Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
Litao Yang Department of Surgery, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
Yiqun Fan Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, Zhejiang, China
Jing Yang Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China
Junjie Huang Department of Surgery, Changxing People's Hospital, Huzhou, Zhejiang, China
Yulian Wu Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China Cancer Center, Zhejiang University, Hangzhou, Zhejiang, China
Tianye Niu Institute of Translational Medicine, Zhejiang University, Hangzhou, Zhejiang, China Department of Radiation Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

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