ORIGINAL RESEARCH article
Front. Mol. Biosci.
Sec. Molecular Diagnostics and Therapeutics
Volume 12 - 2025 | doi: 10.3389/fmolb.2025.1635296
This article is part of the Research TopicExploring the Correlation and Heterogeneity Between Acute and Chronic Diseases: Diagnostic and Therapeutic PerspectivesView all 7 articles
Dynamic contrast-enhanced MRI-based radiomics model of intra-tumoral kinetic heterogeneity for predicting breast cancer molecular subtypes
Provisionally accepted
- 1Wuxi No 2 People's Hospital, Wuxi, China
- 2Wuxi Branch of Zhongda Hospital Southeast University, Wuxi, China
- 3Wuxi No.2 People’s Hospital, Jiangnan University Medical Center, Wuxi, China
- 4GE Healthcare, pudong, China
- 5Bayer Healthcare, pudong, China
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Objectives: This study aims to segment intra-tumoral subregions of breast cancer based on kinetic heterogeneity using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). It also aims to construct a radiomics model of the whole tumor and washout region to predict molecular subtypes and human epidermal growth factor receptor 2 (HER2) status. Methods: A total of 124 patients with biopsy-confirmed breast cancer were randomly divided into training and test sets in a 7:3 ratio. Quantitative analysis of breast cancer kinetic heterogeneity parameters based on DCE-MRI data was performed, dividing tumors into three subregions (Persistent, Washout, and Plateau) according to the type of voxel-level contrast enhancement. Radiomics features of the washout region and the whole tumor were extracted from the first phase of DCE-MRI enhancement. The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used to evaluate the performance of the model. Results: The radiomics model using tumor subregion (washout region) features related to kinetic heterogeneity showed the best performance for differentiating between patients with Luminal, HER2, and HER2 status, with AUC values in the train set of 0.924, 0.876, and 0.816, respectively. Exhibiting an AUC value higher than that obtained with the whole tumor and the kinetic heterogeneity parameters. DCA curves showed that the washout region model was more effective in predicting Luminal and HER2-status subtypes, compared to the whole tumor region model. Conclusion: Radiomics analysis of washout areas from high-resolution DCE-MRI breast scans has the potential to better identify molecular subtypes of breast cancer non-invasively. The tumor is divided into three subregions: persistent, washout, and plateau. The radiomics features of washout regions can predict molecular subtypes. Predicting breast cancer subtypes is more effective from the washout region.
Keywords: breast cancer, Subregions, Kinetic Heterogeneity, Radiomics, Dynamic contrast-enhanced magnetic resonance imaging
Received: 26 May 2025; Accepted: 07 Jul 2025.
Copyright: © 2025 Cheng, Ren, Xu, Duan, Zhang and Bao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhongyuan Bao, Wuxi No.2 People’s Hospital, Jiangnan University Medical Center, Wuxi, China
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