The predictive value of preoperative Multiparametric MRI Radiomics Model for axillary lymph node metastasis in breast cancer

Motivation: Axillary lymph node (ALN) involvement plays a pivotal role in the prognosis of breast cancer(BRCA). Goal(s): This study aims to develop a radiomics model that integrates features from multiparametric MRI to preoperatively diagnose ALN metastasis in BRCA . Approach: Radiomics features were extracted from the multiparametric MRI of 227 patients. Patients were classified into three groups based on the metastatic ALN load .Models were developed using machine learning, with performance evaluated using the area under the curve (AUC). Results: The models achieved AUCs of 0.854, 0.687, 0.825 in the training cohort, and 0.702, 0.522, 0.753 in the validation cohort for three groups, respectively. Impact: These radiomics models serve as a noninvasive tool to predict ALN metastasis in BRCA preoperatively.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1992
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

The predictive value of preoperative Multiparametric MRI Radiomics Model for axillary lymph node metastasis in breast cancer

Xinyu Qian, Wenjia Wang, Ge Lihong
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

The predictive value of preoperative Multiparametric MRI Radiomics Model for axillary lymph node metastasis in breast cancer

Xinyu Qian, Wenjia Wang, Ge Lihong
article en

Abstract

Motivation: Axillary lymph node (ALN) involvement plays a pivotal role in the prognosis of breast cancer(BRCA). Goal(s): This study aims to develop a radiomics model that integrates features from multiparametric MRI to preoperatively diagnose ALN metastasis in BRCA . Approach: Radiomics features were extracted from the multiparametric MRI of 227 patients. Patients were classified into three groups based on the metastatic ALN load .Models were developed using machine learning, with performance evaluated using the area under the curve (AUC). Results: The models achieved AUCs of 0.854, 0.687, 0.825 in the training cohort, and 0.702, 0.522, 0.753 in the validation cohort for three groups, respectively. Impact: These radiomics models serve as a noninvasive tool to predict ALN metastasis in BRCA preoperatively.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 63%
Radiomics and Machine Learning in Medical Imaging
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