Machine-learning-based multimodality radiomics analysis for the preoperative prediction for local relapse in 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.