An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics
Abstract Objective To update the systematic review of radiomics in osteosarcoma. Methods PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 2022. The studies were assessed by Radiomics Q...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-08-01
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| Series: | Insights into Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13244-022-01277-6 |
| _version_ | 1811340411866710016 |
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| author | Jingyu Zhong Yangfan Hu Guangcheng Zhang Yue Xing Defang Ding Xiang Ge Zhen Pan Qingcheng Yang Qian Yin Huizhen Zhang Huan Zhang Weiwu Yao |
| author_facet | Jingyu Zhong Yangfan Hu Guangcheng Zhang Yue Xing Defang Ding Xiang Ge Zhen Pan Qingcheng Yang Qian Yin Huizhen Zhang Huan Zhang Weiwu Yao |
| author_sort | Jingyu Zhong |
| collection | DOAJ |
| description | Abstract Objective To update the systematic review of radiomics in osteosarcoma. Methods PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 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 evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results. Results Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence. Conclusions The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research. |
| first_indexed | 2024-04-13T18:41:33Z |
| format | Article |
| id | doaj.art-361e643d272d4cdf87d578ab5a7aa5cd |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-04-13T18:41:33Z |
| publishDate | 2022-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-361e643d272d4cdf87d578ab5a7aa5cd2022-12-22T02:34:42ZengSpringerOpenInsights into Imaging1869-41012022-08-0113111510.1186/s13244-022-01277-6An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomicsJingyu Zhong0Yangfan Hu1Guangcheng Zhang2Yue Xing3Defang Ding4Xiang Ge5Zhen Pan6Qingcheng Yang7Qian Yin8Huizhen Zhang9Huan 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 Jiao Tong University Affiliated Sixth People’s HospitalDepartment 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, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDepartment of Orthopedics, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDepartment of Pathology, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDepartment of Pathology, Shanghai Jiao Tong University Affiliated Sixth People’s HospitalDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Imaging, Tongren Hospital, Shanghai Jiao Tong University School of MedicineAbstract Objective To update the systematic review of radiomics in osteosarcoma. Methods PubMed, Embase, Web of Science, China National Knowledge Infrastructure, and Wanfang Data were searched to identify articles on osteosarcoma radiomics until May 15, 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 evidence supporting radiomics application for osteosarcoma was rated according to meta-analysis results. Results Twenty-nine articles were included. The average of the ideal percentage of RQS, the TRIPOD adherence rate and the CLAIM adherence rate were 29.2%, 59.2%, and 63.7%, respectively. RQS identified a radiomics-specific issue of phantom study. TRIPOD addressed deficiency in blindness of assessment. CLAIM and TRIPOD both pointed out shortness in missing data handling and sample size or power calculation. CLAIM identified extra disadvantages in data de-identification and failure analysis. External validation and open science were emphasized by all the above three tools. The risk of bias and applicability concerns were mainly related to the index test. The meta-analysis of radiomics predicting neoadjuvant chemotherapy response by MRI presented a diagnostic odds ratio (95% confidence interval) of 28.83 (10.27–80.95) on testing datasets and was rated as weak evidence. Conclusions The quality of osteosarcoma radiomics studies is insufficient. More investigation is needed before using radiomics to optimize osteosarcoma treatment. CLAIM is recommended to guide the design and reporting of radiomics research.https://doi.org/10.1186/s13244-022-01277-6OsteosarcomaRadiomicsMachine learningQuality improvementSystematic review |
| spellingShingle | Jingyu Zhong Yangfan Hu Guangcheng Zhang Yue Xing Defang Ding Xiang Ge Zhen Pan Qingcheng Yang Qian Yin Huizhen Zhang Huan Zhang Weiwu Yao An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics Insights into Imaging Osteosarcoma Radiomics Machine learning Quality improvement Systematic review |
| title | An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics |
| title_full | An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics |
| title_fullStr | An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics |
| title_full_unstemmed | An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics |
| title_short | An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics |
| title_sort | updated systematic review of radiomics in osteosarcoma utilizing claim to adapt the increasing trend of deep learning application in radiomics |
| topic | Osteosarcoma Radiomics Machine learning Quality improvement Systematic review |
| url | https://doi.org/10.1186/s13244-022-01277-6 |
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