MRI radiomics-based machine learning model for complete response classification after chemoradiotherapy in advanced rectal cancer.

Objective: Accurate identification of complete response (CR) after neoadjuvant chemoradiotherapy (NCRT) is essential for selecting candidates for watch-and-wait treatment in advanced rectal cancer. This study aimed to develop and evaluate a magnetic resonance imaging (MRI) radiomics-based machine learning model to classify CR and Non-CR in post-NCRT rectal MRI images. Methods: Using region-of-interest masks, 107 radiomic features were extracted and normalized to a 0-1 range using min-max scaling. Four feature selection methods (ANOVA, RFE, SBS, and LASSO) were paired with four classifiers (LR, SVM, RF, and XGB), and all combinations were evaluated through 5-fold cross-validation in the training set using mean ROC AUC as the comparison metric. Results: Among 16 combinations, RFE + SVM achieved the highest cross-validation AUC of 0.89. On an independent test set, the model achieved a sensitivity of 0.84, specificity of 0.80, accuracy of 0.82, and F1-score of 0.82. The most informative features were intensity and local texture features, including TotalEnergy, 10Percentile, Coarseness, Strength, Correlation, ZoneEntropy, and Idmn. Conclusions: MRI radiomics combined with machine learning shows preliminary promise as an exploratory approach for classifying CR and Non-CR after NCRT in rectal cancer. Given the retrospective, single-center design and the absence of external validation, these findings should be interpreted as preliminary, and prospective multicenter validation is required before clinical implementation. A multicenter external validation integrating endoscopy and digital rectal examination findings is planned to enhance generalizability and clinical utility.