Machine Learning, Radiomics, and Clinical Models Predicting Checkpoint Inhibitor Pneumonitis in Lung Cancer: A Systematic Review and Meta-analysis

= 90.7%) and a wide 95% prediction interval of 0.51-0.95, which is the decision-relevant quantity. By validation design, internal cross-validation pooled to 0.83, internal split-sample to 0.76, and only two studies reported external validation (0.84 and 0.92, described narratively). Imaging-based models (k = 8) showed numerically higher discrimination than clinical models (k = 4; 0.85 versus 0.78, moderator p = 0.46) without a statistically significant difference, and this comparison is confounded by design covariates. Estimates were stable on leave-one-out, alternative-model, cohort-overlap, and standard-error-source sensitivity analyses (range 0.80-0.84). Under the standard Prediction model Risk Of Bias ASsessment Tool, applied independently by two reviewers with consensus resolution, two studies were at high, nine at unclear, and one at low overall risk of bias; certainty of evidence was low (very low under a stricter appraisal scenario, which left the pooled estimate essentially unchanged). Current models separate risk groups on average but remain unready for clinical use; adequately powered, externally validated, and calibration-reported models are needed.