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

Zongbao Li; Hui Dai; Yunxia Liu; Feng Pan; Yanyan Yang; Mengchao Zhang

doi:10.3389/fonc.2021.697497

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

Front Oncol. 2021 Jul 7:11:697497. doi: 10.3389/fonc.2021.697497. eCollection 2021.

Authors

Zongbao Li¹, Hui Dai², Yunxia Liu¹, Feng Pan¹, Yanyan Yang¹, Mengchao Zhang¹

Affiliations

¹ China-Japan Union Hospital of Jilin University, Changchun, China.
² The First Affiliated Hospital of Soochow University, Suzhou, China.

Abstract

Background: Immunotherapy, adjuvant chemotherapy, and prognosis of colorectal cancer are associated with MSI. Biopsy pathology cannot 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 based on T2-weighted and ADC imaging.

Statistical tests: Independent sample t test, Mann-Whitney test, the chi-squared test, Receiver operating characteristic curves, calibration curves, decision curve analysis 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 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 that the combined score had a good calibration degree, and the decision curve demonstrated that the combined score was of benefit for clinical use.

Data conclusion: Radiomics analysis of T2W and ADC images showed significant relevance in the prediction of microsatellite status, and the accuracy of combined model of ADC and T2W features was better than either alone.

Keywords: magnetic resonance; microsatellite instability; multi-sequence MR; radiomics; rectal cancer.