| Autor: |
Jingjing Shao, Hongxin Lin, Lei Ding, Bing Li, Danyang Xu, Yang Sun, Tianming Guan, Haiyang Dai, Ruihao Liu, Demao Deng, Bingsheng Huang, Shiting Feng, Xianfen Diao, Zhenhua Gao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Insights into Imaging, Vol 15, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1869-4101 |
| DOI: |
10.1186/s13244-024-01610-1 |
| Popis: |
Abstract 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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