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Abstract #0228

Radiopathological System for Quantifying the Tumor Microenvironment and Predicting Prognosis in Osteosarcoma: A Multicenter Study

Xiaoxuan Zhang1, Xiaoping Chen2, Ruiling She2, Shisi Li2, Qianjin Feng1, and Yinghua Zhao2
1School of Biomedical Engineering, Southern Medical University, Guangzhou, China, 2Third Affiliated Hospital of Southern Medical University, School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Osteosarcoma, Tumor microenvironment, Radiopathomics

Motivation: Current quantification methods for osteosarcoma tumors and their tumor microenvironment (TME) are often ineffective, resulting in treatment failures and poor patient prognosis.

Goal(s): To develop an automated system integrating MRI and WSI data for precise, quantitative assessment of tumors and TME to improve prognostic predictions.

Approach: We developed a system utilizing MRI and WSI data from a 185-patient, multisite cohort, enabling independent assessment of tumors and TME.

Results: Combined radiopathomic features significantly improve patient outcome predictions.

Impact: This model provides a comprehensive prognostic assessment, essential for advancing predictive tools and extensive validation before clinical application.

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Keywords