Fluid mechanics based quantitative transport mapping network (QTMnet) for predicting treatment response of breast cancer from DCE MRI
Motivation: There lacks a non-invasive breast cancer treatment response prediction method from MRI images. Goal(s): To test the feasibility of using perfusion parameter reconstructed from QTMnet processing of DCE MRI to predict breast cancer treatment response. Approach: QTMnet is trained on synthetically generated perfusion data and tested on 41 breast DCE MRI images. Results: $$$PS$$$ and $$$V_e$$$ map from QTMnet can predict whether breast cancer has complete treatment responses. Impact: QTMnet can be coupled into clinical breast cancer practice to predict treatment response.
Authors
- Benjamin Weppner (ORCID: https://orcid.org/0000-0002-7553-2971)
- Dominick Romano (ORCID: https://orcid.org/0000-0002-8691-406X)
- Renjiu Hu (ORCID: https://orcid.org/0000-0003-4651-3349)
- Pascal Spincemaille (ORCID: https://orcid.org/0000-0002-8821-1341)
- Yi Wang (ORCID: https://orcid.org/0000-0002-7784-5007)
- Thanh D. Nguyen (ORCID: https://orcid.org/0000-0002-1411-7694)
- Qihao Zhang (ORCID: https://orcid.org/0000-0002-8054-5849)
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/10.58530/2025/1978
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00