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

Checkpoint inhibitor pneumonitis (CIP) is a potentially fatal immune-related adverse event of immune checkpoint inhibitor (ICI) therapy in lung cancer, and many machine-learning (ML), computed tomography (CT) radiomics, deep-learning, and clinical models have been proposed to predict it. This systematic review searched PubMed, Embase, Web of Science, CINAHL, and SinoMed, supplemented by citation searching and other sources, with independent dual-reviewer screening throughout and a reproducible PubMed verification search updated to August 16, 2026. Twelve peer-reviewed lung-cancer CIP-occurrence models (4,875 patients, 1,057 events) formed the main analysis; validation C-statistics were pooled on the logit scale with restricted maximum likelihood and Hartung-Knapp intervals, stratified by validation design. The pooled validation C-statistic was 0.82 (95% confidence interval 0.75-0.88) with high heterogeneity (I2 = 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.