The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer

The integration of radiomics features with clinicopathologic characteristics provides a robust predictive tool for ALNM, potentially offering a noninvasive and effective approach for clinical decision-making.

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Publication Details

Journal
Translational Cancer Research
Published
2025-12-01
DOI
https://doi.org/10.21037/tcr-2025-1418
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer

Li Gong, Han Wang
Translational Cancer Research
Radiomics and Machine Learning in Medical Imaging
article

The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer

Li Gong, Han Wang
article en

Abstract

The integration of radiomics features with clinicopathologic characteristics provides a robust predictive tool for ALNM, potentially offering a noninvasive and effective approach for clinical decision-making.

Translational Cancer ResearchVol. 14(12)
Nanjing Drum Tower Hospital (CN)
Peace, Justice and strong institutions
Openalex Percentile: Top 30%
Radiomics and Machine Learning in Medical Imaging
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The value of different machine learning radiomics based on DCE-MRI in predicting axillary lymph node status of breast cancer — Li Gong, Han Wang · Translational Cancer Research (2025) | TGRS Research Map | TGRS