Identification of NPM and non-mass breast cancer based on radiological features and radiomics

Radiomics using PyRadiomics-extracted features, LASSO, and SVM provides a robust quantitative tool to differentiate NPM from non-mass breast cancer, enhancing diagnostic precision and clinical decision-making.

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Publication Details

Journal
Frontiers in Oncology
Published
2025-12-01
DOI
https://doi.org/10.3389/fonc.2025.1665427
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

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article

Identification of NPM and non-mass breast cancer based on radiological features and radiomics

Xingyu Chen, Zhen Guo, Xin Lin, Zhuonan Wang et al.
Frontiers in Oncology
Radiomics and Machine Learning in Medical Imaging
article

Identification of NPM and non-mass breast cancer based on radiological features and radiomics

Xingyu Chen, Zhen Guo, Xin Lin, Zhuonan Wang, Rui Yan, Wei Liu
article en

Abstract

Radiomics using PyRadiomics-extracted features, LASSO, and SVM provides a robust quantitative tool to differentiate NPM from non-mass breast cancer, enhancing diagnostic precision and clinical decision-making.

Frontiers in OncologyVol. 15
Northwest Women's and Children's Hospital (CN), Xi'an Medical University (CN), First Affiliated Hospital of Xi'an Jiaotong University (CN), Xi’an University of Posts and Telecommunications (CN)
Key Research and Development Projects of Shaanxi Province
Openalex Percentile: Top 48%
Radiomics and Machine Learning in Medical Imaging
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