When combined with machine learning techniques, particularly convolutional neural networks (CNNs) and radiomic texture analysis, these approaches can facilitate more accurate tumor grading, subtype differentiation (e.g., osteosarcoma vs. Ewing sarcoma), and prediction of molecular alterations such as TP53 and RB1 mutations [5,6,7,8,9,10]. This evidence concerns the gene RB1 and osteosarcoma.