Keywords: Diagnosis/Prediction, Bone, Osteosarcoma
Motivation: To compare the efficacy of radiomics models using four machine learning algorithms in predicting for local relapse of osteosarcoma before surgery.
Goal(s): This study established a robust prediction model of local relapse to improve prognosis efficacy of osteosarcoma and aid a personalized treatment planning.
Approach: Comparison of four algorithms in classifying high-risk local-relapse patients from low-risk ones based on only preoperative radiographic, MR, and both radiomic features.
Results: The random-forest based prediction model using both radiograph-MRI radiomic features had the best performance on differentiating patients with local relapse from those with non-local relapse with AUC of 0.868, sensitivity of 0.909, specificity of 0.750.
Impact: This study facilitated early identification of high-risk local-relapse osteosarcoma patients who may benefit from model-guided therapeutic interventions and have better long-term outcomes.
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