Figure 2.

Flowchart of the study and the architecture of the RIC model. (A) 1,454 quantitative radiomic features were automatically extracted from manually-segmented tumor regions in T2-weighted imaging data, and key predictive features were subsequently selected using ICC analysis, the variance threshold approach, and the LASSO method. Finally, the performance of the radiomics models was evaluated by ROC analysis. (B) The radiomics and clinical features were extracted from T2-weighted imaging and medical records, respectively. The six selected radiomics features and five clinical features were used as input together for multivariable logistic regression analysis. MSI, microsatellite instability; MSS, microsatellite stability; RIC, radiomics imaging and clinical; ICC, intraclass correlation coefficient; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic.