Research on the application of a multi-model cascaded deep learning framework in the pathological diagnosis of osteosarcoma

The model demonstrated Dice coefficient values of 0.83 or higher in tumor segmentation, osteosarcoma osteoid matrix segmentation, necrotic area segmentation, lung metastatic tumor segmentation, and lung metastatic osteoid matrix segmentation. For necrosis classification, overall osteosarcoma subtypes, and localized osteosarcoma subtypes, the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) all exceeded 90%. The proposed model exhibited excellent performance, indicating high potential for future clinical application in osteosarcoma patients. This framework shows promise for enhancing the precision and efficiency of pathological diagnosis and evaluation in osteosarcoma management.