aceAI-based prediction of immunotherapy outcomes in non-small cell lung cancer using clinical, blood, imaging, pathology and genomic data
Source article: Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC
Abstract: Despite a decade in, immunotherapy (IO) treatment selection in non-small cell lung cancer (NSCLC) remains largely guided by subgroup analyses and imperfect programmed death ligand 1 (PD-L1) and clinical scores. To our knowledge, I 3 LUNG ( NCT05537922 ) is currently the largest international, real-world, multimodal, artificial intelligence (AI)-based study, enrolling 2,396 patients. We integrated real-world clinical and blood (CB) data, computed tomography (CT) images, digital pathology (DP), and genomics into m…
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The I3LUNG study enrolled 2,396 patients with non-small cell lung cancer to develop AI models for immunotherapy selection, integrating clinical and blood data, CT, digital pathology and genomics into early and intermediate fusion models. CB-only models reached AUC up to 0.77 in the independent TEST set and outperformed PD-L1, ECOG PS, NLR, LDH and LIPI, and a usability study found physicians improved predictions with the explainable AI tool.
The findings matter because immunotherapy selection still relies on imperfect biomarkers, and an explainable tool that helps both experts and nonexperts could change practice if validated. Uncertainty remains due to lower AUC of 0.55-0.72 in external validation and lack of translated benefit from multimodal integration, with prospective validation in over 2,000 patients still ongoing at publication.
- I3LUNG (NCT05537922) enrolled 2,396 patients as largest international real-world multimodal AI study in NSCLC.
- CB-only ML and DL models reached AUC up to 0.77 in TEST set.
- Clinical usability study tested XAI ML CB-only tool with lung expert and nonexpert physicians.
- Prospective validation of decision support system in more than 2,000 patients was ongoing as of publication date.
In NSCLC immunotherapy selection, CB-only AI models outperformed PD-L1 and clinical scores in the independent test set, and both expert and nonexpert physicians improved their predictions when using the explainable AI decision support tool.
AI model performance dropped in external validation to AUC 0.55-0.72, and multimodal integration did not show translated incremental benefit in TEST and EXVAL sets.
The rundown
The study integrated real-world clinical and blood data, CT images, digital pathology, and genomics into MLEF and DLIF models, with CB-only models achieving AUC up to 0.77 in TEST.
In the clinical usability study, both expert and nonexpert physicians improved prediction using the XAI ML CB-only tool, while multimodal CB+CT+DP fusion showed higher performance in development but uncertain benefit in independent evaluation.
External generalizability is limited by performance drop in external validation and uncertain incremental benefit of multimodal fusion, which did not translate in TEST and EXVAL.
Sources
- Peer-reviewedNature Medicine2026-09-13
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