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Role of Radiomics in the Prediction of Muscle-invasive Bladder Cancer: A Systematic Review and Meta-analysis

M. Kozikowski, R. Suarez-Ibarrola, R. Osiecki, K. Bilski, C. Gratzke, SF. Shariat, A. Miernik, J. Dobruch

. 2022 ; 8 (3) : 728-738. [pub] 20210605

Jazyk angličtina Země Nizozemsko

Typ dokumentu časopisecké články, metaanalýza, přehledy, systematický přehled

Perzistentní odkaz   https://www.medvik.cz/link/bmc22025449

CONTEXT: Radiomics is a field of science that aims to develop improved methods of medical image analysis by extracting a large number of quantitative features. New data have emerged on the successful application of radiomics and machine-learning techniques to the prediction of muscle-invasive bladder cancer (MIBC). OBJECTIVE: To systematically review the diagnostic performance of radiomic techniques in predicting MIBC. EVIDENCE ACQUISITION: The literature search for relevant studies up to July 2020 was performed in the PubMed and EMBASE databases by two independent reviewers. The meta-analysis was inducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Inclusion criteria comprised studies that evaluated the diagnostic accuracy of radiomic models in predicting MIBC and used pathological examination as the reference standard. For bias assessment, Quality Assessment of Diagnostic Accuracy Studies-2 and Radiomic Quality Score were used. Weighted summary proportions were used to calculate pooled sensitivity and specificity. A linear mixed model was implemented to calculate the hierarchical summary receiver-operating characteristic (HSROC). Meta-regression analyses were performed to explore heterogeneity. EVIDENCE SYNTHESIS: Eight studies with a total of 860 patients were included. The summary estimates for sensitivity and specificity in predicting MIBC were 82% (95% confidence interval [CI]: 77-86%) and 81% (95% CI: 76-85%), respectively. The area under HSROC was 0.88. There were no relevant heterogeneity in diagnostic accuracy measures (I2 = 33% and 41% for sensitivity and specificity, respectively), which was confirmed by a subsequent meta-regression analysis. CONCLUSIONS: Radiomics shows high diagnostic performance in predicting MIBC. Despite differences in approaches, radiomic models were relatively homogeneous in their diagnostic accuracy. With further improvements, radiomics has the potential to become a useful adjunct in clinical management of bladder cancer. PATIENT SUMMARY: Rapidly evolving imaging analysis methods using artificial intelligence algorithms, called radiomics, show high diagnostic performance in predicting muscle-invasive bladder cancer.

Citace poskytuje Crossref.org

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$a CONTEXT: Radiomics is a field of science that aims to develop improved methods of medical image analysis by extracting a large number of quantitative features. New data have emerged on the successful application of radiomics and machine-learning techniques to the prediction of muscle-invasive bladder cancer (MIBC). OBJECTIVE: To systematically review the diagnostic performance of radiomic techniques in predicting MIBC. EVIDENCE ACQUISITION: The literature search for relevant studies up to July 2020 was performed in the PubMed and EMBASE databases by two independent reviewers. The meta-analysis was inducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Inclusion criteria comprised studies that evaluated the diagnostic accuracy of radiomic models in predicting MIBC and used pathological examination as the reference standard. For bias assessment, Quality Assessment of Diagnostic Accuracy Studies-2 and Radiomic Quality Score were used. Weighted summary proportions were used to calculate pooled sensitivity and specificity. A linear mixed model was implemented to calculate the hierarchical summary receiver-operating characteristic (HSROC). Meta-regression analyses were performed to explore heterogeneity. EVIDENCE SYNTHESIS: Eight studies with a total of 860 patients were included. The summary estimates for sensitivity and specificity in predicting MIBC were 82% (95% confidence interval [CI]: 77-86%) and 81% (95% CI: 76-85%), respectively. The area under HSROC was 0.88. There were no relevant heterogeneity in diagnostic accuracy measures (I2 = 33% and 41% for sensitivity and specificity, respectively), which was confirmed by a subsequent meta-regression analysis. CONCLUSIONS: Radiomics shows high diagnostic performance in predicting MIBC. Despite differences in approaches, radiomic models were relatively homogeneous in their diagnostic accuracy. With further improvements, radiomics has the potential to become a useful adjunct in clinical management of bladder cancer. PATIENT SUMMARY: Rapidly evolving imaging analysis methods using artificial intelligence algorithms, called radiomics, show high diagnostic performance in predicting muscle-invasive bladder cancer.
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$a Osiecki, Rafał $u Urology Clinic, Centre of Postgraduate Medical Education, Department of Urology, Professor Witold Orlowski Independent Public Hospital, Warsaw, Poland
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$a Gratzke, Christian $u Department of Urology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany
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$a Miernik, Arkadiusz $u Department of Urology, Faculty of Medicine, University of Freiburg Medical Centre, Freiburg, Germany
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