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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords