Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma

Computed tomography-based radiomics quantification predicts epidermal growth factor receptor mutation status and efficacy of first-line targeted therapy in lung adenocarcinoma

BackgroundBiomarkers that predict the efficacy of first-line tyrosine kinase inhibitors (TKIs) are pivotal in epidermal growth factor receptor (EGFR) mutant advanced lung adenocarcinoma. Imaging-based biomarkers have attracted much attention in anticancer therapy. This study aims to use the machine...

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Bibliographic Details
Main Authors: Meilin Jiang, Pei Yang, Jing Li, Wenying Peng, Xingxiang Pu, Bolin Chen, Jia Li, Jingyi Wang, Lin Wu
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Oncology
Subjects:
lung adenocarcinoma
computed tomography
radiomic response biomarker
epidermal growth factor receptor mutation status
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fonc.2022.985284/full

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