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

作者：Hui Yao, Mengxue Yang, Xin Jiang, Hao Jia, Tao Sun, Molin Li, Taiping Wang, Xuefeng Tang · 发表于：Oncology Reviews · 年份：2025 · DOI：10.3389/or.2025.1592408 · 被引用次数：2 · 研究领域：Sarcoma Diagnosis and Treatment、Digital Imaging for Blood Diseases、AI in cancer detection

Introduction: Osteosarcoma is the most common malignant tumor of bone tissue in adolescents, and precise pathological diagnosis is the primary foundation for establishing the most effective treatment plan. The pathological evaluation of tumor necrosis after chemotherapy is crucial for assessing therapeutic efficacy in osteosarcoma patients. However, pathologists often face several challenges during the diagnosis and evaluation process. Methods: To address these needs, we designed and developed a multi-model cascaded deep learning framework utilizing an advanced Vision Mamba (ViM) model as the core network architecture. The study employed one of the most comprehensive osteosarcoma datasets, sourced from: (1) real-world data from 68 osteosarcoma patients collected at Chongqing General Hospital, and (2) publicly available osteosarcoma assessment data from the University of Texas Southwestern/UT Dallas. Pathological images were annotated using the Palgo pathology image artificial intelligence self-training platform according to algorithm requirements. A triple verification mechanism of annotation, review, and archiving was implemented, and Palgo's integrated interactive algorithm correction mechanism was used to continuously refine the data annotation process. Results and Discussion: 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 l...