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      Development and validation of a radiomics model based on T2WI images for preoperative prediction of microsatellite instability status in rectal cancer : Study Protocol Clinical Trial (SPIRIT Compliant)

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          Globally, colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females. Rectal cancer (RC) accounts for about 28% of all newly diagnosed CRC cases. The treatment of choice for locally advanced RC is a combination of surgical resection and chemotherapy and/or radiotherapy. These patients can potentially be cured, but the clinical outcome depends on the tumor biology. Microsatellite instability (MSI) is an important biomarker in CRC, with crucial diagnostic, prognostic, and predictive implications. It is important to develop a noninvasive, repeatable, and reproducible method to reflect the microsatellite status. Magnetic resonance imaging (MRI) has been recommended as the preferred imaging examination for RC in clinical practice by both the National Comprehensive Cancer Network and the European Society for Medical Oncology guidelines. T2WI is the core sequence of MRI scanning protocol for RC. Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research.

          We proposed a hypothesis: A simple radiomics model based on only T2WI images can accurately evaluate the MSI status of RC preoperatively.


          To develop a radiomics model based on T2WI images for accurate preoperative diagnosis the MSI status of RC.


          All patients with RC were retrospectively enrolled. The dataset was randomly split into training cohort (70% of all patients) and testing cohort (30% of all patients). The radiomics features will be extracted from T2WI–MR images of the entire primary tumor region. Least absolute shrinkage and selection operator was used to select the most predictive radiomics features. Logistic regression models were constructed in the training/validation cohort to discriminate the MSI status using clinical factors, radiomics features, or their integration. The diagnostic performance of these 3 models was evaluated in the testing cohort based on their area under the curve, sensitivity, specificity, and accuracy.


          This study will help us know whether radiomics model based on T2WI images to preoperative identify MSI status of RC.

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          Most cited references 13

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          Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

          To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
            • Record: found
            • Abstract: found
            • Article: found

            ESMO recommendations on microsatellite instability testing for immunotherapy in cancer, and its relationship with PD-1/PD-L1 expression and tumour mutational burden: a systematic review-based approach

              • Record: found
              • Abstract: not found
              • Article: not found

              Rectal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up


                Author and article information

                Medicine (Baltimore)
                Medicine (Baltimore)
                Wolters Kluwer Health
                March 2020
                06 March 2020
                : 99
                : 10
                [a ]Department of Radiology, West China Hospital, Sichuan University, Chengdu
                [b ]Department of Radiology, Sichuan Provincial Corps Hospital, Chinese People’ s Armed Police Forces, Leshan
                [c ]Department of Pathology, West China Hospital, Sichuan University, Chengdu
                [d ]Institute of Advanced Research, Infervision, Beijing, China.
                Author notes
                []Correspondence: Bin Song, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China (e-mail: songlab_radiology@ ).
                MD-D-20-00893 19428
                Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Research Article
                Study Protocol Clinical Trial
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