End-to-End Deep Learning Prediction of Neoadjuvant Chemotherapy Response in Osteosarcoma Patients Using Routine MRI

This study aims to develop an end-to-end deep learning (DL) model to predict neoadjuvant chemotherapy (NACT) response in osteosarcoma (OS) patients using routine magnetic resonance imaging (MRI). We retrospectively analyzed data from 112 patients with histologically confirmed OS who underwent NACT prior to surgery. Multi-sequence MRI data (including T2-weighted and contrast-enhanced T1-weighted images) and physician annotations were utilized to construct an end-to-end DL model. The model integrates ResUNet for automatic tumor segmentation and 3D-ResNet-18 for predicting NACT efficacy. Model performance was assessed using area under the curve (AUC) and accuracy (ACC). Among the 112 patients, 51 exhibited a good NACT response, while 61 showed a poor response. No statistically significant differences were found in age, sex, alkaline phosphatase levels, tumor size, or location between these groups (P > 0.05). The ResUNet model achieved robust performance, with an average Dice coefficient of 0.579 and average Intersection over Union (IoU) of 0.463. The T2-weighted 3D-ResNet-18 classification model demonstrated superior performance in the test set with an AUC of 0.902 (95% CI: 0.766-1), ACC of 0.783, sensitivity of 0.909, specificity of 0.667, and F1 score of 0.800. Our proposed end-to-end DL model can effectively predict NACT response in OS patients using routine MRI, offering a potential tool for clinical decision-making.