Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations

Non-invasive identification of TKI-resistant NSCLC: a multi-model AI approach for predicting EGFR/TP53 co-mutations

Abstract Objectives To investigate the value of multi-model based on preoperative CT scans in predicting EGFR/TP53 co-mutation status. Methods We retrospectively included 2171 patients with non-small cell lung cancer (NSCLC) with pre-treatment computed tomography (CT) scans and predicting epidermal...

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Bibliographic Details
Main Authors: Jiayi Li, Renjie Xu, Dan Wang, Zhanlue Liang, Yangqian Li, Qinglan Wang, Lingfeng Bi, Yawen Qi, Yaojie Zhou, Weimin Li
Format: Article
Language:English
Published: BMC 2025-07-01
Series:BMC Pulmonary Medicine
Subjects:
Non-small-cell lung cancer
Genotype
Machine learning
Radiomics
Multi-model
Online Access:https://doi.org/10.1186/s12890-025-03805-8
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https://doi.org/10.1186/s12890-025-03805-8

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