Abstract
Background:Prostate cancer (PCa) is one of the most common primary malignancies in humans and the second leading cause of cancer-specific mortality among Western males. Computer-aided detection (CAD) systems have been developed for accurate and automated PCa detection and diagnosis, but the diagnostic accuracy of different CAD systems based on magnetic resonance imaging (MRI) for PCa remains controversial. The aim of this study is to systematically review the published evidence to investigate diagnostic accuracy of different CAD systems based on MRI for PCa.Methods:We will conduct the systematic review and meta-analysis according to the Preferred Reporting Items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guidelines. Cochrane library, PubMed, EMBASE and Chinese Biomedicine Literature Database will be systematically searched from inception for eligible articles, 2 independent reviewers will select studies on CAD-based MRI diagnosis of PCa and extract the requisite data. The quality of reporting evidence will be assessed using the quality assessment of diagnosis accuracy study (QUADAS-2) tool. Pooled sensitivity, specificity, and the area under the summary receiver operating characteristic (SROC) curves will be calculated to estimate the diagnostic accuracy of CAD system. In addition, we will conduct subgroup analyses according to the type of classifier of CAD systems used and the different prostate zoon.Results:This study will conduct a meta-analysis of current evidence to investigate the diagnostic accuracy of CAD systems based on MRI for PCa by calculating sensitivity, specificity, and SROC curves.Conclusion:The conclusion of this study will provide evidence to judge whether CAD systems based on MRI have high diagnostic accuracy for PCa.Ethics and dissemination:Ethics approval is not required for this systematic review as it will involve the collection and analysis of secondary data. The results of the review will be reported in international peer-reviewed journals.Prospero registration number:CRD42019132543.
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