Keywords: Bone, Tumor, Neoadjuvant chemotherapy · Response prediction
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.
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