Deep Learning-based Automatic Segmentation and Fusion Radiomics-based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma

Motivation: The efficacy of neoadjuvant chemotherapy (NAC) directly affects the clinical treatment of osteosarcoma (OS) patients. Consequently, it is essential to accurately assess the effectiveness of NAC. Goal(s): To develop an automated method for accurately segmenting tumors and predicting the response to NAC in OS patients from conventional sequences of preoperative MRI. Approach: In the present study, we accomplished two tasks. One involves constructing a deep learning model for automatic tumor segmentation, while the other entails predicting the response to NAC using different feature extraction methods in OS patients. Results: Radiomics models can serve as a non-invasive tool for predicting treatment response in OS. Impact: Radiomics have the potential to non-invasively predict the neoadjuvant chemotherapeutic responses. This tool could significantly contribute to avoiding ineffective chemotherapy and optimizing the management of OS patients in the era of personalized medicine.