Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study

• Published in: Insights into imaging 2024-02, Vol.15 (1), p.35-35, Article 35
• Main Authors: Shao, Jingjing, Lin, Hongxin, Ding, Lei, Li, Bing, Xu, Danyang, Sun, Yang, Guan, Tianming, Dai, Haiyang, Liu, Ruihao, Deng, Demao, Huang, Bingsheng, Feng, Shiting, Diao, Xianfen, Gao, Zhenhua
• Format: Article
• Language: English
• Subjects: Accuracy; Bone cancer; Bone neoplasms; Deep learning; Diagnosis; Diagnostic Radiology; Diagnostic systems; Imaging; Internal Medicine; Interventional Radiology; Knee joint; Medicine; Medicine & Public Health; Model accuracy; Neuroradiology; Original; Original Article; Radiographs; Radiography; Radiology; Sarcoma; Test sets; Tumors; Ultrasound
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-024-01610-1

Objectives To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs. Methods Patients with osteolytic OS and GCT proven by postoperative pathology were retrospectively recruited from four centers (center A, training and internal testing; centers B, C, and D, external testing). Sixteen radiologists with different experiences in musculoskeletal imaging diagnosis were divided into three groups and participated with or without the DL model’s assistance. DL model was generated using EfficientNet-B6 architecture, and the clinical model was trained using clinical variables. The performance of various models was compared using McNemar’s test. Results Three hundred thirty-three patients were included (mean age, 27 years ± 12 [SD]; 186 men). Compared to the clinical model, the DL model achieved a higher area under the curve (AUC) in both the internal (0.97 vs. 0.77, p  = 0.008) and external test set (0.97 vs. 0.64, p