A deep-learning model combining radiomics and clinicopathologic data with breast DCE-MRI improves prediction of complete pathological response after chemotherapy.
Key Details
- 1Study led by Chaowei Wu, PhD, at Cedars-Sinai Medical Center.
- 2Model integrates clinicopathologic data, shape radiomics, and retrospective pharmacokinetic quantification radiomics.
- 3Included MRI data from 1,073 breast cancer patients (2002–2016).
- 4Model achieved higher AUCs (up to 0.82 on external datasets) vs. conventional methods.
- 5Reported accuracy 69%, sensitivity 95%, specificity 59%.
- 6Early pCR prediction aids personalized treatment planning.
Why It Matters
Improved prediction of chemotherapy response using AI-driven analysis of DCE-MRI could enable more personalized and effective treatment strategies in breast cancer, potentially improving outcomes. The results show better accuracy and generalizability than existing methods across multiple datasets.
Source
AuntMinnie
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