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      Radiomics Facilitates Candidate Selection for Irradiation Stents Among Patients With Unresectable Pancreatic Cancer

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          Abstract

          Purpose: To develop a model to select appropriate candidates for irradiation stent placement among patients with unresectable pancreatic cancer with malignant biliary obstruction (UPC-MBO).

          Methods: This retrospective study included 106 patients treated with an irradiation stent for UPC-MBO. These patients were randomly divided into a training group (74 patients) and a validation group (32 patients). A clinical model for predicting restenosis-free survival (RFS) was developed with clinical predictors selected by univariate and multivariate analyses. After integrating the radiomics signature, a combined model was constructed to predict RFS. The predictive performance was evaluated with the concordance index (C-index) in both the training and validation groups. The median risk score of progression in the training group was used to divide patients into high- and low-risk subgroups.

          Results: Radiomics features were integrated with clinical predictors to develop a combined model. The predictive performance was better in the combined model (C-index, 0.791 and 0.779 in the training and validation groups, respectively) than in the clinical model (C-index, 0.673 and 0.667 in the training and validation groups, respectively). According to the median risk score of 1.264, the RFS was significantly different between the high- and low-risk groups ( p < 0.001 for the training group, and p = 0.016 for the validation group).

          Conclusions: The radiomics-based model had good performance for RFS prediction in patients with UPC-MBO who received an irradiation stent. Patients with slow progression should consider undergoing irradiation stent placement for a longer RFS.

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          Most cited references33

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          Pancreatic ductal adenocarcinoma radiology reporting template: consensus statement of the society of abdominal radiology and the american pancreatic association.

          Pancreatic ductal adenocarcinoma is an aggressive malignancy with a high mortality rate. Proper determination of the extent of disease on imaging studies at the time of staging is one of the most important steps in optimal patient management. Given the variability in expertise and definition of disease extent among different practitioners as well as frequent lack of complete reporting of pertinent imaging findings at radiologic examinations, adoption of a standardized template for radiology reporting, using universally accepted and agreed on terminology for solid pancreatic neoplasms, is needed. A consensus statement describing a standardized reporting template authored by a multi-institutional group of experts in pancreatic ductal adenocarcinoma that included radiologists, gastroenterologists, and hepatopancreatobiliary surgeons was developed under the joint sponsorship of the Society of Abdominal Radiologists and the American Pancreatic Association. Adoption of this standardized imaging reporting template should improve the decision-making process for the management of patients with pancreatic ductal adenocarcinoma by providing a complete, pertinent, and accurate reporting of disease staging to optimize treatment recommendations that can be offered to the patient. Standardization can also help to facilitate research and clinical trial design by using appropriate and consistent staging by means of resectability status, thus allowing for comparison of results among different institutions. Copyright © 2014 AGA Institute and RSNA. Published by Elsevier Inc. All rights reserved.
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            CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma – a quantitative analysis

            Background To assess whether CT-derived texture features predict survival in patients undergoing resection for pancreatic ductal adenocarcinoma (PDAC). Methods Thirty patients with pre-operative CT from 2007 to 2012 for PDAC were included. Tumor size and five texture features namely uniformity, entropy, dissimilarity, correlation, and inverse difference normalized were calculated. Mann–Whitney rank sum test was used to compare tumor with normal pancreas. Receiver operating characteristics (ROC) analysis, Cox regression and Kaplan-Meier tests were used to assess association of texture features with overall survival (OS). Results Uniformity (p < 0.001), entropy (p = 0.009), correlation (p < 0.001), and mean intensity (p < 0.001) were significantly different in tumor regions compared to normal pancreas. Tumor dissimilarity (p = 0.045) and inverse difference normalized (p = 0.046) were associated with OS whereas tumor intensity (p = 0.366), tumor size (p = 0.611) and other textural features including uniformity (p = 0.334), entropy (p = 0.330) and correlation (p = 0.068) were not associated with OS. Conclusion CT-derived PDAC texture features of dissimilarity and inverse difference normalized are promising prognostic imaging biomarkers of OS for patients undergoing curative intent surgical resection.
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              CT texture analysis of pancreatic cancer

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                Author and article information

                Contributors
                Journal
                Front Oncol
                Front Oncol
                Front. Oncol.
                Frontiers in Oncology
                Frontiers Media S.A.
                2234-943X
                27 September 2019
                2019
                : 9
                : 973
                Affiliations
                [1] 1Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University , Nanjing, China
                [2] 2School of Life Science and Technology, Xidian University , Xi'an, China
                [3] 3Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences , Beijing, China
                [4] 4University of Chinese Academy of Sciences , Beijing, China
                [5] 5Department of Minimally Invasive Interventional Radiology, Yunnan Tumor Hospital, The Third Affiliated Hospital of Kunming Medical University , Kunming, China
                [6] 6Department of Radiology, Lishui Central Hospital, Wenzhou Medical University , Lishui, China
                [7] 7Department of Interventional Radiology, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China , Hefei, China
                [8] 8Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, School of Medicine, Beihang University , Beijing, China
                [9] 9Engineering Research Centre of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University , Xi'an, China
                Author notes

                Edited by: Rong Tian, Sichuan University, China

                Reviewed by: Zhongxiang Ding, Hangzhou First People's Hospital, China; Xiaohua Zhu, Huazhong University of Science and Technology, China

                *Correspondence: Jie Tian tian@ 123456ieee.org

                This article was submitted to Cancer Imaging and Image-directed Interventions, a section of the journal Frontiers in Oncology

                †These authors have contributed equally to this work

                Article
                10.3389/fonc.2019.00973
                6776612
                bc5550fe-9861-4e4e-b51c-3fc2d8b8ed92
                Copyright © 2019 Zhou, Han, Lu, Wei, Guo, Zhu, Huang, Ji, Lv, Chen, Zhu, Jin, Tian and Teng.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 22 May 2019
                : 13 September 2019
                Page count
                Figures: 5, Tables: 3, Equations: 4, References: 48, Pages: 11, Words: 7018
                Categories
                Oncology
                Original Research

                Oncology & Radiotherapy
                radiomics,pancreatic cancer,malignant biliary obstruction,irradiation stent,survival

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