Risk factors for misclassification in predicting EGFR mutation status using PET/CT imaging in non-small cell lung cancer patients

A plsRglm-based PCS-nomogram model was proposed for the noninvasive prediction of EGFR mutations in NSCLC patients. Compared with smokers, radiomics-based EGFR mutation prediction demonstrated superior performance in never-smokers.

Authors

Zihang Zeng (ORCID: https://orcid.org/0000-0003-1900-8478)
Yong He (ORCID: https://orcid.org/0000-0002-7039-2850)
Jie Chen (ORCID: https://orcid.org/0000-0001-5507-235X)
Hongjun Liu (ORCID: https://orcid.org/0000-0001-5408-2337)
Tianxing Fang
Jiali Li (ORCID: https://orcid.org/0009-0006-3550-5473)

Institutions

Wuhan University (CN)
Zhongnan Hospital of Wuhan University (CN)

Publication Details

Journal: Frontiers in Oncology
Published: 2025-12-03
DOI: https://doi.org/10.3389/fonc.2025.1702905
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 0.00

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article

Risk factors for misclassification in predicting EGFR mutation status using PET/CT imaging in non-small cell lung cancer patients

Zihang Zeng, Yong He, Jie Chen, Hongjun Liu et al.

Dec 3, 2025

Frontiers in Oncology

Radiomics and Machine Learning in Medical Imaging

article

Risk factors for misclassification in predicting EGFR mutation status using PET/CT imaging in non-small cell lung cancer patients

Zihang Zeng, Yong He, Jie Chen, Hongjun Liu, Tianxing Fang, Jiali Li

December 3, 2025article en

Abstract

A plsRglm-based PCS-nomogram model was proposed for the noninvasive prediction of EGFR mutations in NSCLC patients. Compared with smokers, radiomics-based EGFR mutation prediction demonstrated superior performance in never-smokers.

Frontiers in OncologyVol. 15

Wuhan University (CN), Zhongnan Hospital of Wuhan University (CN)

Openalex Percentile: Top 47%

Radiomics and Machine Learning in Medical Imaging

DOI Open Access PDF OpenAlex

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