Fast, Accurate Artificial Intelligence Method to Diagnose and Classify Pediatric Sarcoma Anywhere

An interview with: Adam Thiesen, PhD Candidate, UConn Health, University of Connecticut and the Jackson Laboratory for Genomic Medicine, Farmington,  CT And with: Jayesh Desai MD, Medical Oncologist, Associate Director Clinical Research, Peter MacCallum Cancer Centre, Melbourne, Australia, Co-Chair, AACR Clinical Committee. CHICAGO – An artificial intelligence-based model accurately classified pediatric sarcomas using histopathology images alone, according to study conclusions reported to the American Association for Cancer Research 2025 Annual Meeting. The researchers said it could help provide more patients access to quick, streamlined, and highly accurate cancer diagnoses regardless of their geographic location or health care setting. Audio Journal of Oncology correspondent Peter Goodwin met up with first author of the study, Adam Thiesen, who is a PhD Candidate at UConn Health, University of Connecticut and the Jackson Laboratory for Genomic Medicine in Farmington, Connecticut. For expert comment, Peter Goodwin also talked with Jayesh Desai MD,  Associate Director Clinical Research, Peter MacCallum Cancer Centre, Melbourne, Australia. AACR ABSTRACT Title: “Automated classification of pediatric sarcoma using digital histopathology”

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