CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.

Authors

Yan-Jin Yu
Yaming Wei (ORCID: https://orcid.org/0000-0001-7218-0563)
Qingsong Xu (ORCID: https://orcid.org/0000-0002-0906-7290)
Shuangshuang Chen
Yibing Shi (ORCID: https://orcid.org/0000-0003-4954-313X)
Xuanxuan Zhou (ORCID: https://orcid.org/0000-0003-1117-0964)
Jijun Sun

Institutions

Xuzhou Central Hospital (CN)

Publication Details

Journal: Frontiers in Oncology
Published: 2025-02-13
DOI: https://doi.org/10.3389/fonc.2025.1502932
Citations: 3
Primary Topic: Radiomics and Machine Learning in Medical Imaging
Type: article
Field-Weighted Citation Impact: 4.08

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article

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Yan-Jin Yu, Yaming Wei, Qingsong Xu, Shuangshuang Chen et al.

Feb 13, 2025

3 citations

Frontiers in Oncology

Radiomics and Machine Learning in Medical Imaging

4.08

article

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Yan-Jin Yu, Yaming Wei, Qingsong Xu, Shuangshuang Chen, Yibing Shi, Xuanxuan Zhou, Jijun Sun

February 13, 2025article en

3 citations

Abstract

The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy in distinguishing benign and malignant SPNs.

Frontiers in OncologyVol. 15

Xuzhou Central Hospital (CN)

Good health and well-being

Openalex Percentile: Top 8%

Radiomics and Machine Learning in Medical Imaging

4.08

DOI Open Access PDF OpenAlex

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CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules — Yan-Jin Yu, Yaming Wei, et al. · Frontiers in Oncology (2025) | TGRS Research Map | TGRS