Magnetic resonance imaging-based deep learning model for prediction of the neoadjuvant chemotherapy response and survival prognosis in adolescents with osteosarcoma

Vollständiger Abstract

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BACKGROUND Osteosarcoma is the most common primary bone malignancy in children, adolescents and young adults, and treatment typically includes preoperative neoadjuvant chemotherapy (NAC) followed by surgery. However, up to 20% of patients show resistance to NAC, exposing them to unnecessary toxicity and delayed definitive treatment. Noninvasive imaging biomarkers derived from magnetic resonance imaging (MRI), combined with deep learning (DL) methods, have the potential to capture intratumoral heterogeneity and enable accurate preoperative identification of chemotherapy responders and prediction of long-term outcomes. AIM To evaluate the prediction performance of the MRI-based model using DL methods for identification of response to NAC, and to explore the prognostic value of DL-based models for prediction of long-term survival in adolescents and young adults with osteosarcoma. METHODS All 134 eligible patients were included retrospectively from January 2012 to December 2018, including 94 patients in the training cohort and 40 patients in the testing cohort. We extracted DL-based features via transfer learning methods, and adopted support vector machine for MRI-based models construction evaluated by the area under the receiver operating characteristics curve (AUC). The MRI-based model with the highest AUC value was used to generate the DL-based signature. An integrated prediction model for response to NAC was developed including clinical variables and the DL-based signature. Additionally, the prognostic values of clinical variables and the DL-based signature were measured associated with overall survival (OS) by Cox proportional hazard analysis to develop an integrated prognostic model. RESULTS The integrated prediction model represented great discrimination abilities in the training cohort considering AUC of 0.961, accuracy of 90.43%, sensitivity of 92.45%, and specificity of 87.80%, while indicating AUC of 0.816, accuracy of 70.00%, sensitivity of 54.55%, and specificity of 88.89% in the testing cohort. The significant association has also been indicated in the integrated prognostic model with OS considering great classification and discrimination abilities. CONCLUSION The integrated prediction model represented effective abilities in identification of response to NAC, and the integrated prognostic model showed underlying prognostic value for OS in adolescents and young adults with osteosarcoma.