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      Multi-modal radiomics model to predict treatment response to neoadjuvant chemotherapy for locally advanced rectal cancer

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          Abstract

          BACKGROUND

          Neoadjuvant chemotherapy is currently recommended as preoperative treatment for locally advanced rectal cancer (LARC); however, evaluation of treatment response to neoadjuvant chemotherapy is still challenging.

          AIM

          To create a multi-modal radiomics model to assess therapeutic response after neoadjuvant chemotherapy for LARC.

          METHODS

          This retrospective study consecutively included 118 patients with LARC who underwent both computed tomography (CT) and magnetic resonance imaging (MRI) before neoadjuvant chemotherapy between October 2016 and June 2019. Histopathological findings were used as the reference standard for pathological response. Patients were randomly divided into a training set ( n = 70) and a validation set ( n = 48). The performance of different models based on CT and MRI, including apparent diffusion coefficient (ADC), dynamic contrast enhanced T1 images (DCE-T1), high resolution T2-weighted imaging (HR-T2WI), and imaging features, was assessed by using the receiver operating characteristic curve analysis. This was demonstrated as area under the curve (AUC) and accuracy (ACC). Calibration plots with Hosmer-Lemeshow tests were used to investigate the agreement and performance characteristics of the nomogram.

          RESULTS

          Eighty out of 118 patients (68%) achieved a pathological response. For an individual radiomics model, HR-T2WI performed better (AUC = 0.859, ACC = 0.896) than CT (AUC = 0.766, ACC = 0.792), DCE-T1 (AUC = 0.812, ACC = 0.854), and ADC (AUC = 0.828, ACC = 0.833) in the validation set. The imaging performance for extramural venous invasion detection was relatively low in both the training (AUC = 0.73, ACC = 0.714) and validation (AUC = 0.578, ACC = 0.583) sets. The multi-modal radiomics model reached an AUC of 0.925 and ACC of 0.886 in the training set, and an AUC of 0.93 and ACC of 0.875 in the validation set. For the clinical radiomics nomogram, good agreement was found between the nomogram prediction and actual observation.

          CONCLUSION

          A multi-modal nomogram using traditional imaging features and radiomics of preoperative CT and MRI adds accuracy to the prediction of treatment outcome, and thus contributes to the personalized selection of neoadjuvant chemotherapy for LARC.

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          Most cited references39

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          Machine Learning methods for Quantitative Radiomic Biomarkers

          Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 ± 0.05, AUC = 0.65 ± 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 ± 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
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            Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

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              The TME trial after a median follow-up of 6 years: increased local control but no survival benefit in irradiated patients with resectable rectal carcinoma.

              To investigate the efficacy of preoperative short-term radiotherapy in patients with mobile rectal cancer undergoing total mesorectal excision (TME) surgery. Local recurrence is a major problem in rectal cancer treatment. Preoperative short-term radiotherapy has shown to improve local control and survival in combination with conventional surgery. The TME trial investigated the value of this regimen in combination with total mesorectal excision. Long-term results are reported after a median follow-up of 6 years. One thousand eight hundred and sixty-one patients with resectable rectal cancer were randomized between TME preceded by 5 x 5 Gy or TME alone. No chemotherapy was allowed. There was no age limit. Surgery, radiotherapy, and pathologic examination were standardized. Primary endpoint was local control. Median follow-up of surviving patients was 6.1 year. Five-year local recurrence risk of patients undergoing a macroscopically complete local resection was 5.6% in case of preoperative radiotherapy compared with 10.9% in patients undergoing TME alone (P < 0.001). Overall survival at 5 years was 64.2% and 63.5%, respectively (P = 0.902). Subgroup analyses showed significant effect of radiotherapy in reducing local recurrence risk for patients with nodal involvement, for patients with lesions between 5 and 10 cm from the anal verge, and for patients with uninvolved circumferential resection margins. With increasing follow-up, there is a persisting overall effect of preoperative short-term radiotherapy on local control in patients with clinically resectable rectal cancer. However, there is no effect on overall survival. Since survival is mainly determined by distant metastases, efforts should be directed towards preventing systemic disease.
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                Author and article information

                Contributors
                Journal
                World J Gastroenterol
                World J. Gastroenterol
                WJG
                World Journal of Gastroenterology
                Baishideng Publishing Group Inc
                1007-9327
                2219-2840
                21 May 2020
                21 May 2020
                : 26
                : 19
                : 2388-2402
                Affiliations
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Gastrointestinal Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China. songlab_radiology@ 123456163.com
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Life Science, PDx, IPM team, GE Healthcare, Shanghai 210000, China
                Department of Gastrointestinal Surgery, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
                Author notes

                Author contributions: All authors helped to perform the research; Li ZY wrote the manuscript and performed the procedures and data analysis; Li ZY and Wang XD conceived of and designed the study, and performed the experiments and data analysis; Song B contributed to writing of the manuscript and to conception and design of the study; Li M, Ye Z, Yuan F and Liu XJ contributed to writing of the manuscript; Yuan Y, Xia CC, Li Q and Zhang X performed the data collection and data analysis.

                Supported by Research Grant of National Nature Science Foundation of China, No. 81971571; Multimodal MR Imaging and Radiomics of Rectal Cancer, Science and Technology Department of Sichuan Province, No. 2019YFS0431; and Sichuan University Training Program of Innovation and Entrepreneurship for Undergraduates, No. C2019104739.

                Corresponding author: Bin Song, MD, PhD, Chief Doctor, Professor, Department of Radiology, West China Hospital of Sichuan University, No. 37, Guoxue Alley, Chengdu 610041, Sichuan Province, China. songlab_radiology@ 123456163.com

                Article
                jWJG.v26.i19.pg2388
                10.3748/wjg.v26.i19.2388
                7243642
                32476800
                b637bce0-37db-4b97-8f9f-c728d6f3fd42
                ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.

                This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial.

                History
                : 30 December 2019
                : 27 March 2020
                : 21 April 2020
                Categories
                Retrospective Study

                radiomics,rectal cancer,neoadjuvant chemotherapy,magnetic resonance imaging,computed tomography

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