Authors
Zijian Yang, Taolue Wang, Shilong Liu, Zicheng Zhang, Yibo Zhang, Fan Yang, Bo Yu, Shuaishuai Gao, Yu Chen, Lin Yang, Meng Zhou
Published in
Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77075. Aug 14, 2026. Epub Aug 14, 2026.
Abstract
Brain metastasis (BM) is a major cause of mortality in limited-stage small-cell lung cancer (LS-SCLC). Prophylactic cranial irradiation (PCI) reduces BM incidence but carries neurotoxicity and lacks individualized risk assessment. Here, we developed BraMARS, an explainable deep learning model that estimates future BM risk from routine H&E-stained whole-slide images of resected LS-SCLC. BraMARS demonstrates robust discriminatory performance across independent cohorts, with AUCs ranging from 0.738 to 0.944, and stratifies patients into high-risk and low-risk groups with significantly different disease-free survival, overall survival, and brain metastasis-free survival. Retrospective simulation shows BraMARS-guided risk stratification could reduce PCI exposure in 19.3% of low-risk predicted patients while improving identification of high-risk-predicted patients by 84.4%. Histopathologic attribution and proteomic analyses linked higher scores to distinct tissue patterns and programs involving mitochondrial metabolism, reactive-oxygen-species detoxification, and DNA repair. Overall, BraMARS provides a biologically interpretable histopathology-based framework for estimating subsequent BM risk in resected LS-SCLC, with potential to support individualized intracranial risk assessment, intensified MRI surveillance, and hypothesis generation for prospective BM-prevention strategies.
PMID:
42598747
Bibliographic data and abstract were imported from PubMed on 14 Aug 2026.
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