ArticleFrontiers in molecular biosciences2025
Dynamic contrast-enhanced MRI-based radiomics model of intra-tumoral kinetic heterogeneity for predicting breast cancer molecular subtypes.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- MRI-based radiomics with SHAP interpretation for preoperative prediction of upstaging in ductal carcinomaTranslational cancer research · 2026Article
- ITCNET: a visual and language pretraining model based on multimodal fusion in predicting the breast cancer subtypes on DCE-MRI.BMC medical informatics and decision making · 2026Article
- Multiparametric MRI and artificial intelligence for non-invasive HER2 assessment in breast cancer: a comprehensive review.Frontiers in medicine · 2026Review
- Monitoring Pharmacological Treatment of Breast Cancer with MRI.Current issues in molecular biology · 2025Review
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6 authors.
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Abstract
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.
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