Development and external validation of an interpretable deep learning model for early immune-related adverse events
Hongming Pan1,2, Chunyan Zhao1, Hao Sun2,3
1Department of Oncology, The Affiliated Dazu Hospital of Chongqing Medical University, Chongqing, China.
Objective:
To develop and externally validate an interpretable deep learning model for pre-treatment prediction of early clinically significant immune-related adverse events in patients with hepatocellular carcinoma receiving immune checkpoint inhibitor-based therapy.
Methods:
We conducted a multicenter retrospective cohort study of patients with hepatocellular carcinoma receiving immune checkpoint inhibitor-based therapy, with atezolizumab plus bevacizumab serving as the representative regimen in this cohort. The study population was divided into a training cohort and an independent external validation cohort. Early immune-related adverse events were defined as clinically significant events occurring within three months after treatment initiation. Five prediction models were constructed using baseline clinical and circulating immunological variables obtained prior to treatment, including TabNet, logistic regression, random forest, extreme gradient boosting, and support vector machine models. Model performance was evaluated using discrimination, calibration, and clinical utility metrics, and model interpretability was assessed using feature attribution analyses.
Results:
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.
Conclusions:
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.