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

The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible.

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Publication Details

Journal
PeerJ
Published
2024-12-03
DOI
https://doi.org/10.7717/peerj.18618
Citations
3
Primary Topic
Lung Cancer Treatments and Mutations
Type
article
Field-Weighted Citation Impact
1.26

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article

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

Liping Dai, Zhigang Zhou, Jimin Hao, Man Liu et al.
3 citations
PeerJ
Lung Cancer Treatments and Mutations
1.26
article

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

Liping Dai, Zhigang Zhou, Jimin Hao, Man Liu, Songyun Ouyang, Chunling Zhao
article en
3 citations

Abstract

The developed logistic regression model emerges as a promising tool for forecasting EGFR mutation status. It holds potential to aid clinicians in more precisely identifying patients likely to benefit from EGFR molecular testing and facilitating targeted therapy decision-making, particularly in scenarios where molecular testing is impractical or inaccessible.

PeerJVol. 12
Zhengzhou University (CN), First Affiliated Hospital of Zhengzhou University (CN), Luoyang Orthopedic-Traumatological Hospital of Henan Province (CN)
National Natural Science Foundation of China
Peace, Justice and strong institutions
Openalex Percentile: Top 18%
Lung Cancer Treatments and Mutations
1.26
DOIOpen Access PDFOpenAlex
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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 — Liping Dai, Zhigang Zhou, et al. · PeerJ (2024) | TGRS Research Map | TGRS