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Article

Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico - 1 Aug 2026

Wang Yu, Li Yi-Tong, Wang Ming-Hao, Zhang Cheng-Yi, Jiang Ying, Xu Qi, Liu Ying-Ping, Li Can-Jun, Li Ye-Xiong, Bi Nan

Abstract excerpt

PURPOSE: Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need. While liquid biopsy is widely used, single circulating tumor DNA (ctDNA) shows limited predictive power. We developed an interpretable deep-learning model to quantitatively predict outcomes. METHODS/PATIENTS: We integrated data from 1373 advanced NSCLC patients profiled by two...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 41649698
DOI: 10.1007/s12094-026-04220-z

Genes

TP53
ARID1A
MET
PIK3CA
STK11
EGFR

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026.

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