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 Tool from UCLA Targets Undiagnosed Alzheimer's and Diagnostic Disparity
UCLA researchers developed an AI model using EHR data to better detect undiagnosed Alzheimer's disease, especially in underrepresented groups.
•EurekAlert
AI Model Improves Differentiation of Brain Tumor Progression from Radiation Necrosis on MRI
A York University-led study shows a novel AI using advanced MRI can distinguish between progressive brain tumors and radiation necrosis more accurately than human assessment.
•EurekAlert
AutoML Model Accurately Differentiates Brain Tumors on MRI
Thomas Jefferson University researchers developed an AutoML model that distinguishes pituitary macroadenomas from parasellar meningiomas on MRI with over 97% accuracy.