Thomas, Maya;
Murali, Sanjana;
Simpson, Benjamin Scott S;
Freeman, Alex;
Kirkham, Alex;
Kelly, Daniel;
Whitaker, Hayley C;
... Norris, Joseph M; + view all
(2023)
Use of artificial intelligence in the detection of primary prostate cancer in multiparametric MRI with its clinical outcomes: a protocol for a systematic review and meta-analysis.
BMJ Open
, 13
(8)
, Article e074009. 10.1136/bmjopen-2023-074009.
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Abstract
INTRODUCTION: Multiparametric MRI (mpMRI) has transformed the prostate cancer diagnostic pathway, allowing for improved risk stratification and more targeted subsequent management. However, concerns exist over the interobserver variability of images and the applicability of this model long term, especially considering the current shortage of radiologists and the growing ageing population. Artificial intelligence (AI) is being integrated into clinical practice to support diagnostic and therapeutic imaging analysis to overcome these concerns. The following report details a protocol for a systematic review and meta-analysis investigating the accuracy of AI in predicting primary prostate cancer on mpMRI. METHODS AND ANALYSIS: A systematic search will be performed using PubMed, MEDLINE, Embase and Cochrane databases. All relevant articles published between January 2016 and February 2023 will be eligible for inclusion. To be included, articles must use AI to study MRI prostate images to detect prostate cancer. All included articles will be in full-text, reporting original data and written in English. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols 2015 checklist. The QUADAS-2 score will assess the quality and risk of bias across selected studies. ETHICS AND DISSEMINATION: Ethical approval will not be required for this systematic review. Findings will be disseminated through peer-reviewed publications and presentations at both national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42021293745.
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