Multi-parametric MRI radiomics to predict sensitivity to neoadjuvant chemoradiotherapy based on multi-regions of interest in locally advanced rectal cancer: a multicenter study.

BACKGROUND

To evaluate whether baseline multi-parametric MRI radiomics models, integrating multiple regions of interest (ROIs), can predict pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC), and to validate performance externally.

METHODS

In this multicenter, retrospective cohort study, 510 patients with LARC treated with NCRT followed by total mesorectal excision were enrolled (330 for training, 83 for internal validation, and 97 for external validation). Radiomic features were extracted from three ROIs (primary tumor, mesorectal region, and lateral lymph node regions). After redundancy reduction and clustering, the top 30 features were selected using gradient boosting decision trees via cross-validation. Four classifiers, logistic regression, random forest, support vector machine, and extra trees, were evaluated, with SMOTE applied to address class imbalance. Model performance was compared across ROI combinations, MRI sequences, and algorithms using the area under the receiver operating characteristic curve (AUC).

RESULTS

Overall, 98 of 510 patients (19.2%) achieved pCR. The multi-ROI, multi-parametric model based on extra trees achieved an AUC of 0.717 in the external validation set. Among single-ROI models, tumor ROI yielded the highest AUC (0.630). Among MRI sequences, the ADC-based model performed best (AUC 0.629).

CONCLUSION

Baseline multi-parametric MRI radiomics models integrating multiple ROIs showed modest predictive performance for pCR in LARC and demonstrated similar predictive utility in an independent external cohort. This approach may provide pre-treatment risk stratification and support future research on individualized treatment strategies and organ preservation.