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To mitigate life-threatening radiation-induced lymphopenia in esophageal cancer patients, the authors constructed a weighted ensemble regressor integrated with a novel Cross-Residual Conformal Prediction (CRCP) framework to predict post-chemoradiation Absolute Lymphocyte Count (ALC) nadirs. Reliable uncertainty quantification is vital for high-stakes oncology decisions, such as optimizing radiation dose distributions and personalizing the triage between photon and proton therapies. Validated on a longitudinal cohort of 1,491 patients, the ensemble achieved an ROC-AUC of 0.78 for Grade 4 immunosuppression classification (MAE 0.0926 K/uL), while CRCP provided narrow, patient-level prediction intervals backed by distribution-free coverage guarantees.
Predicting life-threatening radiation-induced immunosuppression no longer requires uncalibrated point estimates: cross-residual conformal prediction delivers tight, mathematically guaranteed uncertainty intervals for individualized toxicity risk.
Severe radiation-induced lymphopenia (RIL), specifically Grade 4 RIL (G4RIL), is a frequent immune toxicity and a strong predictor of poor survival for esophageal cancer patients undergoing concurrent chemoradiation therapy. This study developed an uncertainty-incorporated ensemble machine learning model to predict Absolute Lymphocyte Count (ALC) nadir using baseline clinical and dosimetric variables, supporting treatment decisions such as proton therapy selection and dose distribution optimization. A cohort of 1,491 esophageal cancer patients treated with photon or proton therapy between July 2001 and April 2022 was analyzed. A weighted ensemble model was constructed using clinical features and dosimetric variables, including the composite dosimetric score (CDS) derived from radiation dose distributions. To quantify individual-level predictive uncertainty, Cross-Residual Conformal Prediction (CRCP) was developed and evaluated against standard conformal prediction methods. Proton therapy patients maintained a significantly higher mean ALC nadir than photon patients (0.33 vs. 0.25 K/uL). The ensemble model achieved a mean absolute error of 0.0926 K/uL for ALC nadir prediction and an ROC-AUC of 0.78 for G4RIL classification. Baseline ALC, Planning Target Volume, and CDS were the most influential predictors. CRCP achieved competitive prediction interval widths with mathematically guaranteed coverage, offering a favorable balance between interval width and coverage accuracy. This framework enables risk-aware, personalized clinical decision-making in radiation oncology.