A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers
Abstract Purpose To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. Methods We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to i...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | Journal of Orthopaedic Surgery and Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13018-023-03863-w |
| _version_ | 1797806563968155648 |
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| author | Jingyu Zhong Yue Xing Guangcheng Zhang Yangfan Hu Defang Ding Xiang Ge Zhen Pan Qian Yin Huizhen Zhang Qingcheng Yang Huan Zhang Weiwu Yao |
| author_facet | Jingyu Zhong Yue Xing Guangcheng Zhang Yangfan Hu Defang Ding Xiang Ge Zhen Pan Qian Yin Huizhen Zhang Qingcheng Yang Huan Zhang Weiwu Yao |
| author_sort | Jingyu Zhong |
| collection | DOAJ |
| description | Abstract Purpose To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. Methods We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented. Results Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present. Conclusion The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application. |
| first_indexed | 2024-03-13T06:09:10Z |
| format | Article |
| id | doaj.art-3f66d2c74c71467aa387b690948bcf5b |
| institution | Directory Open Access Journal |
| issn | 1749-799X |
| language | English |
| last_indexed | 2024-03-13T06:09:10Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Orthopaedic Surgery and Research |
| spelling | doaj.art-3f66d2c74c71467aa387b690948bcf5b2023-06-11T11:20:26ZengBMCJournal of Orthopaedic Surgery and Research1749-799X2023-06-0118111510.1186/s13018-023-03863-wA systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkersJingyu Zhong0Yue Xing1Guangcheng Zhang2Yangfan Hu3Defang Ding4Xiang Ge5Zhen Pan6Qian Yin7Huizhen Zhang8Qingcheng Yang9Huan Zhang10Weiwu Yao11Department of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Sports Medicine, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Orthopedics, Tongren Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pathology, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Pathology, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Orthopedics, Shanghai Sixth People’s Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineAbstract Purpose To systematically assess the quality of radiomics research in giant cell tumor of bone (GCTB) and to test the feasibility of analysis at the level of radiomics feature. Methods We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data to identify articles of GCTB radiomics until 31 July 2022. The studies were assessed by radiomics quality score (RQS), transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) statement, checklist for artificial intelligence in medical imaging (CLAIM), and modified quality assessment of diagnostic accuracy studies (QUADAS-2) tool. The radiomic features selected for model development were documented. Results Nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 26%, 56%, and 57%, respectively. The risk of bias and applicability concerns were mainly related to the index test. The shortness in external validation and open science were repeatedly emphasized. In GCTB radiomics models, the gray level co-occurrence matrix features (40%), first order features (28%), and gray-level run-length matrix features (18%) were most selected features out of all reported features. However, none of the individual feature has appeared repeatably in multiple studies. It is not possible to meta-analyze radiomics features at present. Conclusion The quality of GCTB radiomics studies is suboptimal. The reporting of individual radiomics feature data is encouraged. The analysis at the level of radiomics feature has potential to generate more practicable evidence for translating radiomics into clinical application.https://doi.org/10.1186/s13018-023-03863-wGiant cell tumor of boneRadiomicsMachine learningDifferential diagnosisQuality improvementSystematic review |
| spellingShingle | Jingyu Zhong Yue Xing Guangcheng Zhang Yangfan Hu Defang Ding Xiang Ge Zhen Pan Qian Yin Huizhen Zhang Qingcheng Yang Huan Zhang Weiwu Yao A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers Journal of Orthopaedic Surgery and Research Giant cell tumor of bone Radiomics Machine learning Differential diagnosis Quality improvement Systematic review |
| title | A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| title_full | A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| title_fullStr | A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| title_full_unstemmed | A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| title_short | A systematic review of radiomics in giant cell tumor of bone (GCTB): the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| title_sort | systematic review of radiomics in giant cell tumor of bone gctb the potential of analysis on individual radiomics feature for identifying genuine promising imaging biomarkers |
| topic | Giant cell tumor of bone Radiomics Machine learning Differential diagnosis Quality improvement Systematic review |
| url | https://doi.org/10.1186/s13018-023-03863-w |
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