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.
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.
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.
CRD42021293745.
BMJ open. 2023 Aug 22*** epublish ***
Maya Thomas, Sanjana Murali, Benjamin Scott S Simpson, Alex Freeman, Alex Kirkham, Daniel Kelly, Hayley C Whitaker, Yi Zhao, Mark Emberton, Joseph M Norris
School of Medicine, Imperial College London, London, UK ., School of Medicine, Imperial College London, London, UK., UCL Cancer Institute, University College London, London, UK., Department of Pathology, University College London Hospitals NHS Foundation Trust, London, UK., Department of Radiology, University College London Hospitals NHS Foundation Trust, London, UK., School of Healthcare Sciences, Cardiff University, Cardiff, UK., UCL Division of Surgery and Interventional Science, University College London, London, UK.