The Role of Multiparametric MRI Radiomics in Predicting Axillary Lymph Node Metastasis in Invasive Breast Cancer Patients: A Comparative Study

Motivation: The predictive value of different MRI sequences for axillary lymph node metastasis (ALNM) in invasive breast cancer patients remains uncertain. Goal(s): To compare and assess the performance of individual and combined MRI sequences in preoperatively predicting ALNM status in invasive breast cancer patients. Approach: Three single-sequence models and four multi-sequence models based on multiparametric MRI radiomics were constructed, and their performances were compared using the DeLong test. The optimal radiomics model was then used to create a nomogram incorporating the significant clinicopathologic features. Results: The multi-sequence models outperform the single-sequence models, with dynamic contrast-enhanced imaging showing greater stability among the single-sequence models. Impact: The comparative analysis identified the optimal combination of MRI sequences that can enhance the accuracy of preoperative ALNM status prediction in invasive breast cancer patients, potentially enabling the personalization of axillary treatment strategies.

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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/10.58530/2025/1557
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

The Role of Multiparametric MRI Radiomics in Predicting Axillary Lymph Node Metastasis in Invasive Breast Cancer Patients: A Comparative Study

Yuan Guo, Xinqing Jiang, Yongxin Chen, Yongzhou Xu et al.
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 Role of Multiparametric MRI Radiomics in Predicting Axillary Lymph Node Metastasis in Invasive Breast Cancer Patients: A Comparative Study

Yuan Guo, Xinqing Jiang, Yongxin Chen, Yongzhou Xu, Wei Tang, Si–Yi Chen, Qingcong Kong
article en

Abstract

Motivation: The predictive value of different MRI sequences for axillary lymph node metastasis (ALNM) in invasive breast cancer patients remains uncertain. Goal(s): To compare and assess the performance of individual and combined MRI sequences in preoperatively predicting ALNM status in invasive breast cancer patients. Approach: Three single-sequence models and four multi-sequence models based on multiparametric MRI radiomics were constructed, and their performances were compared using the DeLong test. The optimal radiomics model was then used to create a nomogram incorporating the significant clinicopathologic features. Results: The multi-sequence models outperform the single-sequence models, with dynamic contrast-enhanced imaging showing greater stability among the single-sequence models. Impact: The comparative analysis identified the optimal combination of MRI sequences that can enhance the accuracy of preoperative ALNM status prediction in invasive breast cancer patients, potentially enabling the personalization of axillary treatment strategies.

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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