Radiomics of Voxel-Wise DCE-MRI Wash-In and Wash-Out Maps Enable Quantitative Assessment of Hemodynamic Heterogeneity within Breast Lesions
Motivation: Tumor hemodynamic heterogeneity offers potential for characterizing, diagnosing, and prognosticating breast lesions. Current methods primarily address either spatial morphological heterogeneity or temporal aspects. Goal(s): This study aims to develop a method that visualizes and quantifies both temporal and spatial hemodynamic heterogeneity simultaneously to enhance breast lesion management. Approach: We propose generating voxel-wise time-intensity curve (TIC) corresponding wash-in and wash-out parameter maps to visualize the spatial distribution of temporal information. Radiomics is then used to extract features that quantify hemodynamic heterogeneity. Results: The study successfully demonstrated the clinical utility (AUC = 0.9846) of these quantitative features in differentiating benign from malignant lesions. Impact: This novel approach provides an intuitive and self-explainable visualization and quantification of spatial and temporal hemodynamic heterogeneity, with potential applications across a broader range of clinical settings.
Authors
- Binze Han (ORCID: https://orcid.org/0000-0002-9800-7168)
- Kexin Chen (ORCID: https://orcid.org/0000-0002-8737-8975)
- Jie Wen (ORCID: https://orcid.org/0000-0001-9554-2379)
- Qian Wan (ORCID: https://orcid.org/0000-0001-9243-8939)
- Wei Cui (ORCID: https://orcid.org/0000-0002-3356-5054)
- Dehong Luo (ORCID: https://orcid.org/0000-0001-7902-386X)
- Na Zhang (ORCID: https://orcid.org/0000-0001-9510-4520)
- Ya Ren
- Zhou Liu (ORCID: https://orcid.org/0000-0001-9863-6653)
- Meng Wang (ORCID: https://orcid.org/0000-0001-5823-254X)
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/10.58530/2025/4129
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00