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Leveraging Large Language Models to Extract Prognostic Pathology Features in Ewing Sarcoma
0
Zitationen
16
Autoren
2026
Jahr
Abstract
LLM-assisted extraction of pathology variables is highly accurate and scalable, capable of unlocking "dark data" from historical clinical trials. We identified NSE as a potent risk factor and S100 as a protective marker in Ewing sarcoma, particularly in localized disease. These findings suggest that AI-derived histologic data can refine risk stratification and, if validated, warrant inclusion in future prospective trials.
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Autoren
Institutionen
- Southwestern Medical Center(US)
- The University of Texas Southwestern Medical Center(US)
- Children's Medical Center(US)
- Children's Hospital Colorado(US)
- University of Colorado Denver(US)
- Riley Hospital for Children(US)
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center(US)
- C. S. Mott Children's Hospital(US)