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 Accelerates Radiopharmaceuticals, Boosts Personalized Dosimetry in Cancer
Machine learning is driving advancements in radiopharmaceutical drug discovery and optimizing patient-specific dosimetry for precision cancer therapy.
•EurekAlert
Physicians Overly Trust Erroneous AI, Ignore Contradictory Evidence
Physicians tend to trust incorrect AI advice, even when evidence contradicts it, suggesting risks in clinical decision-making with AI tools.
•EurekAlert
Concerns Raised Over Unverified Datasets in AI Health Prediction Models
A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.