ArticleFrontiers in immunology2026
Predicting response to immune checkpoint inhibitor plus chemotherapy in EGFR-mutant lung adenocarcinoma following first-generation TKI resistance: a multicenter deep learning study.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Patients with epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma who develop resistance to first-generation tyrosine kinase inhibitors (TKIs) without a T790M mutation face a therapeutic dilemma with limited and suboptimal options. Methods: In this multicenter retrospective study, the final analyzable modeling cohort included 490 patients with complete eligible CT imaging and outcome labels, comprising a training cohort of 326 patients, validation cohort 1 of 70 patients, and validation cohort 2 of 94 patients. Model performance was evaluated using AUC, decision curve analysis, and PFS stratification analyses. Results: The 2.5D axial model achieved AUCs of 0.885, 0.819, and 0.863 in the training cohort, validation cohort 1, and validation cohort 2, respectively, based on the locked source prediction files. Conclusion: A CT-based 2.5D deep learning model showed promising performance for treatment-response prediction after EGFR-TKI resistance, but prospective validation and clinical-variable benchmarking remain necessary before clinical implementation.
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