Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels

Predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients through logistic regression: a model incorporating clinical characteristics, computed tomography (CT) imaging features, and tumor marker levels

Background Approximately 60% of Asian populations with non-small cell lung cancer (NSCLC) harbor epidermal growth factor receptor (EGFR) gene mutations, marking it as a pivotal target for genotype-directed therapies. Currently, determining EGFR mutation status relies on DNA sequencing of histologica...

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Bibliographic Details
Main Authors: Jimin Hao, Man Liu, Zhigang Zhou, Chunling Zhao, Liping Dai, Songyun Ouyang
Format: Article
Language:English
Published: PeerJ Inc. 2024-12-01
Series:PeerJ
Subjects:
Non-small cell lung cancer (NSCLC)
Epidermal growth factor receptor (EGFR)
Clinical characteristics
CT imaging features
Tumor marker levels
Logistic regression
Online Access:https://peerj.com/articles/18618.pdf
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