Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs : a multicenter study
© 2024. The Author(s)..
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 < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051).
CONCLUSION: The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs.
CRITICAL RELEVANCE STATEMENT: The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors.
KEY POINTS: • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists'. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:15 |
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| Enthalten in: |
Insights into imaging - 15(2024), 1 vom: 07. Feb., Seite 35 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shao, Jingjing [VerfasserIn] |
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| Links: |
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| Themen: |
Bone neoplasms |
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| Anmerkungen: |
Date Revised 10.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1186/s13244-024-01610-1 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368105288 |
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| 100 | 1 | |a Shao, Jingjing |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs |b a multicenter study |
| 264 | 1 | |c 2024 | |
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| 500 | |a Date Revised 10.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024. The Author(s). | ||
| 520 | |a OBJECTIVES: To develop a deep learning (DL) model for differentiating between osteolytic osteosarcoma (OS) and giant cell tumor (GCT) on radiographs | ||
| 520 | |a 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 | ||
| 520 | |a 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 < 0.001). In the total test set (including the internal and external test sets), the DL model achieved higher accuracy than the junior expert committee (93.1% vs. 72.4%; p < 0.001) and was comparable to the intermediate and senior expert committee (93.1% vs. 88.8%, p = 0.25; 87.1%, p = 0.35). With DL model assistance, the accuracy of the junior expert committee was improved from 72.4% to 91.4% (p = 0.051) | ||
| 520 | |a CONCLUSION: The DL model accurately distinguished osteolytic OS and GCT with better performance than the junior radiologists, whose own diagnostic performances were significantly improved with the aid of the model, indicating the potential for the differential diagnosis of the two bone tumors on radiographs | ||
| 520 | |a CRITICAL RELEVANCE STATEMENT: The deep learning model can accurately distinguish osteolytic osteosarcoma and giant cell tumor on radiographs, which may help radiologists improve the diagnostic accuracy of two types of tumors | ||
| 520 | |a KEY POINTS: • The DL model shows robust performance in distinguishing osteolytic osteosarcoma and giant cell tumor. • The diagnosis performance of the DL model is better than junior radiologists'. • The DL model shows potential for differentiating osteolytic osteosarcoma and giant cell tumor | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Bone neoplasms | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Knee joint | |
| 650 | 4 | |a Radiography | |
| 700 | 1 | |a Lin, Hongxin |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Bing |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Danyang |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Yang |e verfasserin |4 aut | |
| 700 | 1 | |a Guan, Tianming |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Haiyang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Ruihao |e verfasserin |4 aut | |
| 700 | 1 | |a Deng, Demao |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Bingsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Shiting |e verfasserin |4 aut | |
| 700 | 1 | |a Diao, Xianfen |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Zhenhua |e verfasserin |4 aut | |
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