CT-Based Deep Foundation Model for Predicting Immune Checkpoint Inhibitor-Induced Pneumonitis Risk in Lung Cancer

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Abstract

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer therapy but can cause serious immune-related adverse events (irAEs), with pneumonitis (ICI-P) being among the most severe. Early identification of high-risk patients before ICI initiation is critical for close monitoring, timely intervention, and optimizing outcomes. PurposeTo develop and validate a deep learning foundation model to predict ICI-P from baseline CT scans in patients with lung cancer. MethodsWe designed the Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR (CIPHER), a deep learning-powered foundation model combining contrastive learning with a transformer-based masked autoencoder to predict ICI-P from baseline CT scans in lung cancer patients. Using self-supervised learning, CIPHER was pretrained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients, to learn representations of heterogeneous lung parenchyma. Following pretraining, CIPHER was adapted to the internal MDA NSCLC immunotherapy cohort of 347 patients, of whom 33 developed adjudicated ICI-P. Fine-tuning was performed using 254 non-ICI-P patients only, and a held-out internal validation set of 93 patients, including 33 ICI-P cases and 60 non-ICI-P controls, was reserved for evaluation. CIPHER was benchmarked against clinical, radiomics, and ensemble comparator models and externally validated in an independent Johns Hopkins NSCLC cohort of 116 patients, including 20 ICI-P cases and 96 non-ICI-P controls. ResultsIn our internal immunotherapy cohort, CIPHER consistently distinguished patients at elevated risk of ICI-P from those without the event, with AUCs ranging from 0.77 to 0.85. In head-to-head benchmarking, CIPHER achieved an AUC of 0.83, outperforming the clinical, radiomics and ensemble models. In the external validation cohort, CIPHER maintained high performance (AUC=0.83; balanced accuracy=81.7%), exceeding the radiomics model (DeLong p=0.0318) and demonstrating superior specificity without sacrificing sensitivity. By contrast, the radiomics model, despite high sensitivity (85.0%), showed markedly lower specificity (45.8%). Confusion matrix analyses confirmed CIPHERs robust classification, correctly identifying 80 of 96 non-ICI-P cases and 16 of 20 ICI-P cases. ConclusionsWe developed and externally validated CIPHER, a CT-based imaging biomarker for pretreatment ICI-P risk stratification in NSCLC. CIPHER shows promise as a noninvasive tool for ICI-P risk assessment but warrants prospective validation before clinical translation. HighlightsO_LIThe first chest CT AI foundation model for immune toxicity - We introduce CIPHER (Checkpoint-Inhibitor Pneumonitis Hazard EstimatoR), a transformer-based masked autoencoder trained through self-supervised contrastive learning on 590,284 CT slices from 4,242 CT scans from NSCLC patients. This large-scale pretraining enables CIPHER to learn intrinsic lung parenchymal representations linked to immune toxicity risk. C_LIO_LIEarly risk prediction prior to therapy initiation - CIPHER predicts the likelihood of ICI-induced pneumonitis directly from baseline CT scans, offering the first non-invasive foundation model for early risk assessment before ICI. C_LIO_LIRobust validation and benchmarking - We fine-tuned and evaluated CIPHER across independent internal and external NSCLC immunotherapy cohorts, achieving AUCs of 0.77-0.85 internal and 0.83 external testing, surpassing comparator models in both performance and generalizability. C_LIO_LIInterpretability and translational potential - We demonstrate how model-derived attention maps align with clinically relevant pulmonary patterns, enhancing interpretability. C_LIO_LITranslational potential - CIPHERs performance and scalability underscore its potential as a decision-support tool to guide treatment planning, pre-emptive monitoring, and toxicity mitigation in immunotherapy practice. C_LI

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