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Article

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study.

Respiratory research - 27 May 2022

Tan Xueyun, Li Yuan, Wang Sufei, Xia Hui, Meng Rui, Xu Juanjuan, Duan Yanran, Li Yan, Yang Guanghai, Ma Yanling, Jin Yang

Abstract excerpt

BACKGROUND: Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology...

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Identifiers

PMID: 35624472
DOI: 10.1186/s12931-022-02053-2
PMCID: PMC9145462

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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