Supplementary file 1_Development and external validation of an interpretable deep learning model for early immune-related adverse events in hepatocellular carcinoma.docx

Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and model interpretability was assessed using feature attribution analyses.</p>Results<p>Among all models, the TabNet model showed the most favorable overall performance profile, considering discrimination, calibration, clinical utility, and external validation performance. Calibration analysis showed good agreement between predicted risks and observed event rates, while decision curve analysis and clinical impact assessment indicated meaningful clinical utility across a range of threshold probabilities.

Interpretability analyses identified the CD4 to CD8 ratio, serum immunoglobulin G level, albumin, platelet count, and the proportion of CD19-positive B cells as key contributors to model-predicted early immune-related adverse event risk. Risk-stratified feature clustering further suggested that immune-related features and host functional reserve may represent complementary dimensions associated with model-predicted early immune-related toxicity risk.</p>Conclusions<p>An interpretable deep learning model was developed and externally validated for pre-treatment prediction of early clinically significant immune-related adverse events in hepatocellular carcinoma patients receiving immune checkpoint inhibitors.</p>