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

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 to close monitoring, enable timely intervention, and optimize 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 pre-trained on 590,284 CT slices from 2,500 non-small cell lung cancer (NSCLC) patients, to understand heterogeneous lung parenchyma. Following pre-training, the model was fine-tuned on an internal NSCLC cohort for ICI-P risk prediction, with images from 254 patients used for model development and from 93 patients for internal validation. We compared CIPHER with classical radiomic models. We also validated CIPHER on an external NSCLC cohort of 116 patients. 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 radiomic model. In the external validation cohort, CIPHER maintained high performance (AUC=0.83; balanced accuracy=81.7%), exceeding the radiomic models (Delong p=0.0318) and demonstrating superior specificity without sacrificing sensitivity. By contrast, radiomic 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 for predicting future risk of developing ICI-P from pre-treatment CT scans. With prospective validation, CIPHER can be incorporated into routine patient management to improve outcomes. 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 NSCLC patients scans. 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- C_LIO_LI0.85 internal cross validation and 0.83 external testing, surpassing conventional radiomic models in both performance and generalizability. C_LIO_LIInterpretability and clinical readiness - We demonstrate how model-derived attention maps align with clinically relevant pulmonary patterns, enhancing interpretability and enabling seamless integration into radiology workflows. C_LIO_LITranslational potential - CIPHERs performance and scalability underscore its potential as decision-support tool to guide treatment planning, pre-emptive monitoring, and toxicity mitigation in immunotherapy practice. C_LI