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Modeling the Pretest Probability of Identifying Druggable Mutations in Lung Cancer Using Nationwide Comprehensive Genomic Profiling Data.

JCO clinical cancer informatics - 1 Mar 2026

Ikushima Hiroaki, Watanabe Kousuke, Shinozaki-Ushiku Aya, Kodera Satoshi, Takeda Norihiko, Oda Katsutoshi, Kage Hidenori

Abstract excerpt

PURPOSE: Comprehensive genomic profiling (CGP) is a key strategy in precision medicine for lung cancer, yet its clinical implementation remains limited, partly because of the uncertainty in identifying druggable mutations in individual patients. In this study, we investigated the potential of an artificial intelligence (AI)-based tool to predict the probability of identifying druggable mutations before CGP...

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PMID: 41855448
DOI: 10.1200/CCI-25-00269

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PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

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