MRI-Based Mathematical Modeling to Predict the Response of I-SPY 2 Patients with Breast Cancer to Neoadjuvant Therapy
Our tumor forecasting pipeline can accurately predict tumor status after an NAT course-on a patient-specific basis, without a training dataset-using "real-world" MRI data obtained from a multi-subtype, multisite clinical trial.
Authors
- Gaiane M. Rauch (ORCID: https://orcid.org/0000-0003-2079-5202)
- Chengyue Wu (ORCID: https://orcid.org/0000-0002-3789-404X)
- Thomas E. Yankeelov (ORCID: https://orcid.org/0000-0001-6201-3913)
- Rania M. Mohamed (ORCID: https://orcid.org/0000-0002-6487-3965)
- Reshmi J. S. Patel (ORCID: https://orcid.org/0000-0002-6813-3091)
- Casey E. Stowers (ORCID: https://orcid.org/0000-0002-7157-6248)
- Jingfei Ma (ORCID: https://orcid.org/0000-0003-2827-8891)
Institutions
- Livestrong Foundation (US)
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas at Austin (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Clinical Cancer Research
- Published
- 2025-08-26
- DOI
- https://doi.org/10.1158/1078-0432.ccr-25-0668
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00