A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility
Abstract Objectives We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. Methods Original articles investigating radiomic...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-07-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-023-01464-z |
| _version_ | 1797784584956411904 |
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| author | Meng-Lin Huang Jing Ren Zheng-Yu Jin Xin-Yu Liu Yong-Lan He Yuan Li Hua-Dan Xue |
| author_facet | Meng-Lin Huang Jing Ren Zheng-Yu Jin Xin-Yu Liu Yong-Lan He Yuan Li Hua-Dan Xue |
| author_sort | Meng-Lin Huang |
| collection | DOAJ |
| description | Abstract Objectives We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. Methods Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. Results Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range − 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50–49.13) and 12.55 (95% CI 8.38–18.77), respectively. Conclusion Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. Critical relevance statement Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications. Graphical abstract |
| first_indexed | 2024-03-13T00:41:59Z |
| format | Article |
| id | doaj.art-d4bc642efa9245d1b60fb4557f1a5187 |
| institution | Directory Open Access Journal |
| issn | 1869-4101 |
| language | English |
| last_indexed | 2024-03-13T00:41:59Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj.art-d4bc642efa9245d1b60fb4557f1a51872023-07-09T11:15:46ZengSpringerOpenInsights into Imaging1869-41012023-07-0114111910.1186/s13244-023-01464-zA systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utilityMeng-Lin Huang0Jing Ren1Zheng-Yu Jin2Xin-Yu Liu3Yong-Lan He4Yuan Li5Hua-Dan Xue6Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeDepartment of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Obstetric & Gynecologic DiseasesDepartment of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical CollegeAbstract Objectives We aimed to present the state of the art of CT- and MRI-based radiomics in the context of ovarian cancer (OC), with a focus on the methodological quality of these studies and the clinical utility of these proposed radiomics models. Methods Original articles investigating radiomics in OC published in PubMed, Embase, Web of Science, and the Cochrane Library between January 1, 2002, and January 6, 2023, were extracted. The methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses were performed to compare the methodological quality, baseline information, and performance metrics. Additional meta-analyses of studies exploring differential diagnoses and prognostic prediction in patients with OC were performed separately. Results Fifty-seven studies encompassing 11,693 patients were included. The mean RQS was 30.7% (range − 4 to 22); less than 25% of studies had a high risk of bias and applicability concerns in each domain of QUADAS-2. A high RQS was significantly associated with a low QUADAS-2 risk and recent publication year. Significantly higher performance metrics were observed in studies examining differential diagnosis; 16 such studies as well as 13 exploring prognostic prediction were included in a separate meta-analysis, which revealed diagnostic odds ratios of 25.76 (95% confidence interval (CI) 13.50–49.13) and 12.55 (95% CI 8.38–18.77), respectively. Conclusion Current evidence suggests that the methodological quality of OC-related radiomics studies is unsatisfactory. Radiomics analysis based on CT and MRI showed promising results in terms of differential diagnosis and prognostic prediction. Critical relevance statement Radiomics analysis has potential clinical utility; however, shortcomings persist in existing studies in terms of reproducibility. We suggest that future radiomics studies should be more standardized to better bridge the gap between concepts and clinical applications. Graphical abstracthttps://doi.org/10.1186/s13244-023-01464-zOvarian neoplasmsDifferential diagnosisMachine learningSystematic review |
| spellingShingle | Meng-Lin Huang Jing Ren Zheng-Yu Jin Xin-Yu Liu Yong-Lan He Yuan Li Hua-Dan Xue A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility Insights into Imaging Ovarian neoplasms Differential diagnosis Machine learning Systematic review |
| title | A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility |
| title_full | A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility |
| title_fullStr | A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility |
| title_full_unstemmed | A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility |
| title_short | A systematic review and meta-analysis of CT and MRI radiomics in ovarian cancer: methodological issues and clinical utility |
| title_sort | systematic review and meta analysis of ct and mri radiomics in ovarian cancer methodological issues and clinical utility |
| topic | Ovarian neoplasms Differential diagnosis Machine learning Systematic review |
| url | https://doi.org/10.1186/s13244-023-01464-z |
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