Initial results from an ECOG-ACRIN and Caris Life Sciences collaboration show AI-driven multimodal models can more accurately predict recurrence risk in early-stage breast cancer.
Key Details
- 1Collaboration leverages ECOG-ACRIN's clinical trial data and Caris' molecular profiling and imaging AI platforms.
- 2Over 4,000 TAILORx trial patient cases used to train and validate new multimodal deep learning models.
- 3Models integrate histopathologic slide imaging, clinical, and expanded gene expression data.
- 4AI models outperformed established recurrence risk assessment methods, especially for late recurrence (after 5 years).
- 5Potential shown for a scalable, cost-effective diagnostic test based on routine histology and clinical data rather than solely on genomic assays.
- 6Results presented at the 2023 San Antonio Breast Cancer Symposium.
Why It Matters
The study demonstrates unprecedented large-scale integration of imaging, molecular, and clinical data for recurrence prognostication, charting a path toward more personalized and precise breast cancer management. Imaging AI's role in such multimodal frameworks signifies a promising advance for the development of new diagnostic tools in oncologic pathology.
Source
EurekAlert
Related News
•EurekAlert
AI Predicts Risks for Outpatient Stem Cell Therapy in Myeloma
Researchers use machine learning to predict adverse events during stem cell therapy for multiple myeloma, improving outpatient safety.
•EurekAlert
USC Unveils Joint Biomedical Engineering Department Bridging Medicine, Engineering, and Imaging
USC's medical and engineering schools launch a joint biomedical engineering department to accelerate interdisciplinary research and innovation, including imaging and AI.
•EurekAlert
AI-Enhanced CT Heart Fat Measurement Boosts Cardiovascular Risk Prediction
AI-derived measurement of heart fat from CT scans significantly improves long-term cardiovascular disease risk prediction.