Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis
Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differ...
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MDPI AG
2021-03-01
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| Series: | Cancers |
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| Online Access: | https://www.mdpi.com/2072-6694/13/6/1348 |
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| author | Julia Mühlbauer Luisa Egen Karl-Friedrich Kowalewski Maurizio Grilli Margarete T. Walach Niklas Westhoff Philipp Nuhn Fabian C. Laqua Bettina Baessler Maximilian C. Kriegmair |
| author_facet | Julia Mühlbauer Luisa Egen Karl-Friedrich Kowalewski Maurizio Grilli Margarete T. Walach Niklas Westhoff Philipp Nuhn Fabian C. Laqua Bettina Baessler Maximilian C. Kriegmair |
| author_sort | Julia Mühlbauer |
| collection | DOAJ |
| description | Radiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, <i>p</i> < 0.001), 3.08 (95%-CI 2.09–4.06, <i>p</i> < 0.001) and 3.57 (95%-CI 2.69–4.45, <i>p</i> < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, <i>p</i> < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies. |
| first_indexed | 2024-03-10T13:10:16Z |
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| language | English |
| last_indexed | 2024-03-10T13:10:16Z |
| publishDate | 2021-03-01 |
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| series | Cancers |
| spelling | doaj.art-0cad35b59a9945b989b8b0c0f9638a9a2023-11-21T10:48:23ZengMDPI AGCancers2072-66942021-03-01136134810.3390/cancers13061348Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-AnalysisJulia Mühlbauer0Luisa Egen1Karl-Friedrich Kowalewski2Maurizio Grilli3Margarete T. Walach4Niklas Westhoff5Philipp Nuhn6Fabian C. Laqua7Bettina Baessler8Maximilian C. Kriegmair9Department of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyLibrary of the Medical Faculty Mannheim of the University of Heidelberg, University Medical Center Mannheim, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091 Zurich, SwitzerlandInstitute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Raemistrasse 100, 8091 Zurich, SwitzerlandDepartment of Urology and Urological Surgery, University Medical Center Mannheim, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, GermanyRadiomics may increase the diagnostic accuracy of medical imaging for localized and metastatic RCC (mRCC). A systematic review and meta-analysis was performed. Doing so, we comprehensively searched literature databases until May 2020. Studies investigating the diagnostic value of radiomics in differentiation of localized renal tumors and assessment of treatment response to ST in mRCC were included and assessed with respect to their quality using the radiomics quality score (RQS). A total of 113 out of 1098 identified studies met the criteria and were included in qualitative synthesis. Median RQS of all studies was 13.9% (5.0 points, IQR 0.25–7.0 points), and RQS increased over time. Thirty studies were included into the quantitative synthesis: For distinguishing angiomyolipoma, oncocytoma or unspecified benign tumors from RCC, the random effects model showed a log odds ratio (OR) of 2.89 (95%-CI 2.40–3.39, <i>p</i> < 0.001), 3.08 (95%-CI 2.09–4.06, <i>p</i> < 0.001) and 3.57 (95%-CI 2.69–4.45, <i>p</i> < 0.001), respectively. For the general discrimination of benign tumors from RCC log OR was 3.17 (95%-CI 2.73–3.62, <i>p</i> < 0.001). Inhomogeneity of the available studies assessing treatment response in mRCC prevented any meaningful meta-analysis. The application of radiomics seems promising for discrimination of renal tumor dignity. Shared data and open science may assist in improving reproducibility of future studies.https://www.mdpi.com/2072-6694/13/6/1348renal cell carcinomacomputed tomographymagnetic resonance imagingmachine learningradiomics |
| spellingShingle | Julia Mühlbauer Luisa Egen Karl-Friedrich Kowalewski Maurizio Grilli Margarete T. Walach Niklas Westhoff Philipp Nuhn Fabian C. Laqua Bettina Baessler Maximilian C. Kriegmair Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis Cancers renal cell carcinoma computed tomography magnetic resonance imaging machine learning radiomics |
| title | Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis |
| title_full | Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis |
| title_fullStr | Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis |
| title_short | Radiomics in Renal Cell Carcinoma—A Systematic Review and Meta-Analysis |
| title_sort | radiomics in renal cell carcinoma a systematic review and meta analysis |
| topic | renal cell carcinoma computed tomography magnetic resonance imaging machine learning radiomics |
| url | https://www.mdpi.com/2072-6694/13/6/1348 |
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