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Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study
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...
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| Published in: | Insights into imaging 2024-02, Vol.15 (1), p.35-35, Article 35 |
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| container_title | Insights into imaging |
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| creator | 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 |
| description | 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
|
| doi_str_mv | 10.1186/s13244-024-01610-1 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_14be88222aa44028b6e5b0d7e619c955</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_14be88222aa44028b6e5b0d7e619c955</doaj_id><sourcerecordid>2923324875</sourcerecordid><originalsourceid>FETCH-LOGICAL-c492t-cb9fa4a13bb1adff4b169d0c9209652bf017261cdfe01ff8ca45a9f50ab3bb943</originalsourceid><addsrcrecordid>eNp9ks1u1TAQhSMEolXpC7BAltiwCdiO82M2CJW_SpXYwNqaJONcXxL7YieV7jPw0kyaUloWREpsec75xnZOlj0X_LUQTfUmiUIqlXNJr6gEz8Wj7JQKOleCi8f35ifZeUp7Tk9RiKIpnmYn9JXkr0-zXx8QD2xEiN75gdkQWe-sxYh-djC74FmwLKQZw3icXbdNE8QuTMDA92xw4GfW4TiyeZnIDzEstD7vkP3wiGwfHAkIFKF3YYhw2KW3DNi0jASkPhhZmpf--Cx7YmFMeH47nmXfP338dvElv_r6-fLi_VXeKS3nvGu1BQWiaFsBvbWqFZXueacl11UpW8tFLSvR9Ra5sLbpQJWgbcmhJYtWxVl2uXH7AHtziG6CeDQBnLlZCHEwEGlrIxqhWmwaKSWAUlw2bYVly_saK6E7XZbEerexDks7Yb8eJ8L4APqw4t3ODOHaCN6omjeSCK9uCTH8XDDNZnJpvU7wGJZkpJYF_eqmXpu9_Ee6D0v0dFerSlZVXdWcVHJTdTGkFNHe7UZws2bHbNkxlB1zkx0jyPTi_jnuLH-SQoJiEyQq-QHj397_wf4Gx1XSSw</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2922667670</pqid></control><display><type>article</type><title>Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study</title><source>Publicly Available Content Database</source><source>Springer Nature - SpringerLink Journals - Fully Open Access </source><source>PubMed Central</source><creator>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</creator><creatorcontrib>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</creatorcontrib><description>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.
Graphical Abstract</description><identifier>ISSN: 1869-4101</identifier><identifier>EISSN: 1869-4101</identifier><identifier>DOI: 10.1186/s13244-024-01610-1</identifier><identifier>PMID: 38321327</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>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</subject><ispartof>Insights into imaging, 2024-02, Vol.15 (1), p.35-35, Article 35</ispartof><rights>The Author(s) 2024</rights><rights>2024. The Author(s).</rights><rights>The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c492t-cb9fa4a13bb1adff4b169d0c9209652bf017261cdfe01ff8ca45a9f50ab3bb943</cites><orcidid>0009-0000-6254-8707</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2922667670/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2922667670?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38321327$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shao, Jingjing</creatorcontrib><creatorcontrib>Lin, Hongxin</creatorcontrib><creatorcontrib>Ding, Lei</creatorcontrib><creatorcontrib>Li, Bing</creatorcontrib><creatorcontrib>Xu, Danyang</creatorcontrib><creatorcontrib>Sun, Yang</creatorcontrib><creatorcontrib>Guan, Tianming</creatorcontrib><creatorcontrib>Dai, Haiyang</creatorcontrib><creatorcontrib>Liu, Ruihao</creatorcontrib><creatorcontrib>Deng, Demao</creatorcontrib><creatorcontrib>Huang, Bingsheng</creatorcontrib><creatorcontrib>Feng, Shiting</creatorcontrib><creatorcontrib>Diao, Xianfen</creatorcontrib><creatorcontrib>Gao, Zhenhua</creatorcontrib><title>Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study</title><title>Insights into imaging</title><addtitle>Insights Imaging</addtitle><addtitle>Insights Imaging</addtitle><description>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.
Graphical Abstract</description><subject>Accuracy</subject><subject>Bone cancer</subject><subject>Bone neoplasms</subject><subject>Deep learning</subject><subject>Diagnosis</subject><subject>Diagnostic Radiology</subject><subject>Diagnostic systems</subject><subject>Imaging</subject><subject>Internal Medicine</subject><subject>Interventional Radiology</subject><subject>Knee joint</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Model accuracy</subject><subject>Neuroradiology</subject><subject>Original</subject><subject>Original Article</subject><subject>Radiographs</subject><subject>Radiography</subject><subject>Radiology</subject><subject>Sarcoma</subject><subject>Test sets</subject><subject>Tumors</subject><subject>Ultrasound</subject><issn>1869-4101</issn><issn>1869-4101</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9ks1u1TAQhSMEolXpC7BAltiwCdiO82M2CJW_SpXYwNqaJONcXxL7YieV7jPw0kyaUloWREpsec75xnZOlj0X_LUQTfUmiUIqlXNJr6gEz8Wj7JQKOleCi8f35ifZeUp7Tk9RiKIpnmYn9JXkr0-zXx8QD2xEiN75gdkQWe-sxYh-djC74FmwLKQZw3icXbdNE8QuTMDA92xw4GfW4TiyeZnIDzEstD7vkP3wiGwfHAkIFKF3YYhw2KW3DNi0jASkPhhZmpf--Cx7YmFMeH47nmXfP338dvElv_r6-fLi_VXeKS3nvGu1BQWiaFsBvbWqFZXueacl11UpW8tFLSvR9Ra5sLbpQJWgbcmhJYtWxVl2uXH7AHtziG6CeDQBnLlZCHEwEGlrIxqhWmwaKSWAUlw2bYVly_saK6E7XZbEerexDks7Yb8eJ8L4APqw4t3ODOHaCN6omjeSCK9uCTH8XDDNZnJpvU7wGJZkpJYF_eqmXpu9_Ee6D0v0dFerSlZVXdWcVHJTdTGkFNHe7UZws2bHbNkxlB1zkx0jyPTi_jnuLH-SQoJiEyQq-QHj397_wf4Gx1XSSw</recordid><startdate>20240207</startdate><enddate>20240207</enddate><creator>Shao, Jingjing</creator><creator>Lin, Hongxin</creator><creator>Ding, Lei</creator><creator>Li, Bing</creator><creator>Xu, Danyang</creator><creator>Sun, Yang</creator><creator>Guan, Tianming</creator><creator>Dai, Haiyang</creator><creator>Liu, Ruihao</creator><creator>Deng, Demao</creator><creator>Huang, Bingsheng</creator><creator>Feng, Shiting</creator><creator>Diao, Xianfen</creator><creator>Gao, Zhenhua</creator><general>Springer Vienna</general><general>Springer Nature B.V</general><general>SpringerOpen</general><scope>C6C</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>8AO</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>M0S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0009-0000-6254-8707</orcidid></search><sort><creationdate>20240207</creationdate><title>Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study</title><author>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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c492t-cb9fa4a13bb1adff4b169d0c9209652bf017261cdfe01ff8ca45a9f50ab3bb943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Accuracy</topic><topic>Bone cancer</topic><topic>Bone neoplasms</topic><topic>Deep learning</topic><topic>Diagnosis</topic><topic>Diagnostic Radiology</topic><topic>Diagnostic systems</topic><topic>Imaging</topic><topic>Internal Medicine</topic><topic>Interventional Radiology</topic><topic>Knee joint</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Model accuracy</topic><topic>Neuroradiology</topic><topic>Original</topic><topic>Original Article</topic><topic>Radiographs</topic><topic>Radiography</topic><topic>Radiology</topic><topic>Sarcoma</topic><topic>Test sets</topic><topic>Tumors</topic><topic>Ultrasound</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shao, Jingjing</creatorcontrib><creatorcontrib>Lin, Hongxin</creatorcontrib><creatorcontrib>Ding, Lei</creatorcontrib><creatorcontrib>Li, Bing</creatorcontrib><creatorcontrib>Xu, Danyang</creatorcontrib><creatorcontrib>Sun, Yang</creatorcontrib><creatorcontrib>Guan, Tianming</creatorcontrib><creatorcontrib>Dai, Haiyang</creatorcontrib><creatorcontrib>Liu, Ruihao</creatorcontrib><creatorcontrib>Deng, Demao</creatorcontrib><creatorcontrib>Huang, Bingsheng</creatorcontrib><creatorcontrib>Feng, Shiting</creatorcontrib><creatorcontrib>Diao, Xianfen</creatorcontrib><creatorcontrib>Gao, Zhenhua</creatorcontrib><collection>Springer Nature OA/Free Journals</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ProQuest Pharma Collection</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Insights into imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shao, Jingjing</au><au>Lin, Hongxin</au><au>Ding, Lei</au><au>Li, Bing</au><au>Xu, Danyang</au><au>Sun, Yang</au><au>Guan, Tianming</au><au>Dai, Haiyang</au><au>Liu, Ruihao</au><au>Deng, Demao</au><au>Huang, Bingsheng</au><au>Feng, Shiting</au><au>Diao, Xianfen</au><au>Gao, Zhenhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study</atitle><jtitle>Insights into imaging</jtitle><stitle>Insights Imaging</stitle><addtitle>Insights Imaging</addtitle><date>2024-02-07</date><risdate>2024</risdate><volume>15</volume><issue>1</issue><spage>35</spage><epage>35</epage><pages>35-35</pages><artnum>35</artnum><issn>1869-4101</issn><eissn>1869-4101</eissn><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
< 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.
Graphical Abstract</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><pmid>38321327</pmid><doi>10.1186/s13244-024-01610-1</doi><tpages>1</tpages><orcidid>https://orcid.org/0009-0000-6254-8707</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Publicly Available Content Database; Springer Nature - SpringerLink Journals - Fully Open Access ; PubMed Central |
| 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 |
| title | Deep learning for differentiation of osteolytic osteosarcoma and giant cell tumor around the knee joint on radiographs: a multicenter study |
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