ArticleiScience2026
CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study.
Article in iScience, 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Accurate risk assessment after EGFR-TKI resistance is important for guiding subsequent management of patients with EGFR-mutant lung adenocarcinoma. In this multicenter retrospective study, we developed and externally validated a computed tomography (CT)-based deep learning model using pretreatment CT images from 525 patients. A 2.5D ResNet-101 model showed consistent performance across training and external validation cohorts and enabled risk stratification for progression-free and overall survival. The deep learning score remained an independent prognostic factor after adjustment for clinical variables and demonstrated a continuous association with survival risk. These findings support the use of imaging-based deep learning approaches for individualized prognostic assessment in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance.
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