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
Related News
•AuntMinnie
Deep Learning AI Outperforms Radiologists in Detecting ENE on CT
A deep learning tool, DeepENE, exceeded radiologist performance in identifying lymph node extranodal extension in head and neck cancers using preoperative CT scans.
•Radiology Business
Patients Favor AI in Imaging Diagnostics, Hesitate on Triage Use
Survey finds most patients support AI in diagnostic imaging but are reluctant about its use in triage decisions.
•AuntMinnie
AI Projected to Reshape Radiologist Workload But Not Eliminate Jobs
Stanford researchers predict AI could reduce radiologist hours by up to 49% over the next five years, though workforce size is likely to remain stable due to rising imaging demand.