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.