AUTHOR=Li Zongbao , Dai Hui , Liu Yunxia , Pan Feng , Yang Yanyan , Zhang Mengchao 

TITLE=Radiomics Analysis of Multi-Sequence MR Images For Predicting Microsatellite Instability Status Preoperatively in Rectal Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.697497

DOI=10.3389/fonc.2021.697497

ISSN=2234-943X

ABSTRACT=Background: Immunotherapy, adjuvant chemotherapy and prognosis of colorectal cancer are associated with MSI. Biopsy pathology can not fully reflect the MSI status and Heterogeneity of rectal cancer.
Purpose:To develop a radiomic-based model to preoperatively predict MSI status in rectal cancer on MRI.Assessment: The patients were divided into two cohorts (training and testing) at a 7:3 ratio. Radiomics features,including intensity, texture and shape, were extracted from the segmented volumes of interest (VOIs) based on T2-weighted and ADC imaging.
Statistical tests: Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic (ROC) curves (AUC), calibration curves, Decision curve analysis (DCA) and multi-variate logistic regression analysis 
Results: The radiomics models were significantly associated with MSI status. The T2-based model showed an area under the curve (AUC) of 0.870 with 95% CI: 0.794-0.945 (accuracy, 0.845; specificity, 0.714; sensitivity, 0.976) in training set and 0.895 with 95% CI: 0.777-1.000 (accuracy, 0.778; specificity, 0.887; sensitivity, 0.772) in testing set. The ADC-based model had an AUC of 0.790 with 95% CI: 0.794-0.945 (accuracy, 0.774; specificity, 0.714; sensitivity, 0.976) in training set and 0.796 with 95% CI: 0.777-1.000 (accuracy, 0.778; specificity, 0.889; sensitivity, 0.772) in testing set. The combined model integrating T2 and ADC features showed an AUC of 0.908 with 95% CI: 0.845-0.971 (accuracy, 0.857; specificity, 0.762; sensitivity, 0.952) in training set and 0.926 with 95% CI: 0.813-1.000 (accuracy, 0.852; specificity, 1.000; sensitivity, 0.778) in testing set. Calibration curve showed the combine score had a good calibration degree and the decision curve demonstrated the combine score was benefit for clinical use.