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

Figure 2 Feature heatmaps of representative patients on the deep learning ResNet50 algorithm via the Guided Gradient-weighted Class Activation Mapping in adolescents and young adults with osteosarcoma. A: Pathological good response (pGR); B: Non-pGR. The original magnetic resonance imaging images and their corresponding feature heatmaps were shown from left to right. The red color highlighted the region of interest to classify pGR and non-pGR to neoadjuvant chemotherapy. The red color focused on different area for pGR (A) and non-pGR (B) on T1-weighted magnetic resonance imaging sequence (left) and fat-saturated T2-weighted magnetic resonance imaging sequence (right) magnetic resonance imaging images, respectively. The concentration of these heatmaps preferred central area for non-pGR images, and lesion boundaries for pGR images. pGR: Pathological good response; T1WI: T1-weighted magnetic resonance imaging sequence; CAM: Class activation mapping; T2FS: Fat-saturated T2-weighted magnetic resonance imaging sequence.