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AI Model Stratifies Late Recurrence Risk in HR-Positive Breast Cancer
The AI model uses histopathologic imaging, clinical, and molecular data to improve prediction of late distant recurrence in early-stage HR+ breast cancer, validated on 4,300 patients.
- On Dec. 10, 2025, ECOG-ACRIN Cancer Research Group and Caris Life Sciences presented initial findings at the San Antonio Breast Cancer Symposium, releasing the announcement from Philadelphia and Irving, Texas.
- Because clinical factors alone proved insufficient, researchers developed a multimodal algorithm presented by Eleftherios Mamounas, MD, MPH to improve risk stratification and guide extended endocrine therapy in early-stage hormone receptor–positive breast cancer using the TAILORx trial tissue biorepository.
- Using 4,462 TAILORx specimens, researchers validated a model combining pathomic imaging and a 42-gene panel, with Dr. Sparano saying, `'Although the TAILORx trial was the first randomized trial to establish the role of the 21-gene recurrence score...`
- In external validation, the model's performance showed robust late distant recurrence prognostication in 4,300 TAILORx patients and identified those who could forgo additional therapy after standard 5-year adjuvant endocrine therapy.
- Given roughly 310,720 new U.S. breast cancer cases annually, the work could support potential clinical utility, while noting regulatory and validation challenges for diagnostic test development, Caris warned.
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AI Model Stratifies Late Recurrence Risk in HR-Positive Breast Cancer
(MedPage Today) -- At the San Antonio Breast Cancer Symposium, researchers presented findings on Clarity BCR, a multimodal multitask deep-learning algorithm that aims to estimate late distant recurrence risk in hormone receptor (HR)-positive breast...
·New York, United States
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Mason City Globe Gazette+14 Reposted by 14 other sources
ECOG-ACRIN and Caris Life Sciences unveil first findings from a multi-year collaboration to advance AI-powered multimodal tools for breast cancer recurrence risk stratification
New AI models integrating imaging, clinical, and molecular data from the TAILORx tissue biorepository show stronger prognostic performance than current methods to predict recurrence risk in early-stage breast cancer and guide long-term treatment decisions
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