[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR  Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.

Authors

Shanlin Fu (ORCID: https://orcid.org/0000-0002-6238-3612)
Shaodi Wen (ORCID: https://orcid.org/0000-0001-7951-0123)
Jiaqiang Zhang (ORCID: https://orcid.org/0000-0002-0174-6803)
Bo Shen (ORCID: https://orcid.org/0000-0001-7470-4543)
Ru Li (ORCID: https://orcid.org/0000-0001-6660-5633)
Xiaoyue Du

Institutions

Inner Mongolia University (CN)
Jiangsu Cancer Hospital (CN)

Publication Details

Journal: PubMed
Published: 2025-02-20
DOI: https://doi.org/10.3779/j.issn.1009-3419.2025.102.05
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article

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article

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR  Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Shanlin Fu, Shaodi Wen, Jiaqiang Zhang, Bo Shen et al.

Feb 20, 2025

PubMed

Radiomics and Machine Learning in Medical Imaging

article

[Exploration of the Predictive Value of Peripheral Blood-related Indicators for EGFR  Mutations and Prognosis in Non-small Cell Lung Cancer Using Machine Learning].

Shanlin Fu, Shaodi Wen, Jiaqiang Zhang, Bo Shen, Ru Li, Xiaoyue Du

February 20, 2025article en

Abstract

The interpretable model constructed in this study provides a new approach for the prediction of EGFR mutation status in NSCLC patients, which provides a scientific basis for the diagnosis and treatment of patients who cannot undergo genetic testing.

PubMedVol. 28(2)

Inner Mongolia University (CN), Jiangsu Cancer Hospital (CN)

Radiomics and Machine Learning in Medical Imaging

DOI Open Access PDF OpenAlex

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