Prediction of Pathological Complete Response after Chemoradiation for Locally Advanced Rectal Cancer Using Machine Learning with Clinical Features, the INTERCEPTOR Study Preliminary Model.

Abstract

Background: Organ-preserving strategies are increasingly used in rectal cancer for those achieving a pathological complete response (pCR) following chemoradiotherapy (CRT). In locally advanced rectal cancer (LARC), CRT may not significantly impact local recurrence or overall survival when an R0 resection is feasible. Accurate pretreatment prediction of pCR is critical to assist patients in deciding on CRT when it may not be oncologically required. The initial phase of the ongoing INTERCEPTOR study evaluated whether machine learning (ML) models using pretreatment clinical variables could improve pCR prediction beyond the baseline probability of 15-20%.

Methods: Patients with LARC who received CRT followed by total mesorectal excision between 2004 and 2018 at a tertiary referral center were analyzed. Eligible patients received ≥25 fractions of CRT (50.0-50.4 Gy with concomitant capecitabine) and surgery ≥6 weeks after CRT. Extreme gradient boosting (XGBoost) models were trained with 5-fold cross-validation. Model performance was assessed using AUROC, sensitivity, and specificity. Feature importance was assessed with Shapley additive explanations (SHAP) and XGBoost feature importance.

Results: Among 238 included patients, 30 (12.6%) achieved pCR. The number of radiologically positive lymph nodes was the strongest single predictor, but with limited discriminative power (AUROC 0.65 ± 0.04, sensitivity 0.83 ± 0.15 and specificity 0.45 ± 0.1). Combining positive node count with additional clinical variables led to a modest improvement in performance. SHAP analysis confirmed positive lymph node count was the most influential predictor.

Conclusion: Pretreatment clinical variables alone provide poor-to-fair accuracy for predicting pCR after CRT in LARC.