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Review
. 2021 Jul 1;13(13):3318.
doi: 10.3390/cancers13133318.

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies

Tom Syer  1 , Pritesh Mehta  2 , Michela Antonelli  3 , Sue Mallett  1 , David Atkinson  1 , Sébastien Ourselin  3 , Shonit Punwani  1
Affiliations
Review

Artificial Intelligence Compared to Radiologists for the Initial Diagnosis of Prostate Cancer on Magnetic Resonance Imaging: A Systematic Review and Recommendations for Future Studies

Tom Syer et al. Cancers (Basel). .

Abstract

Computer-aided diagnosis (CAD) of prostate cancer on multiparametric magnetic resonance imaging (mpMRI), using artificial intelligence (AI), may reduce missed cancers and unnecessary biopsies, increase inter-observer agreement between radiologists, and alleviate pressures caused by rising case incidence and a shortage of specialist radiologists to read prostate mpMRI. However, well-designed evaluation studies are required to prove efficacy above current clinical practice. A systematic search of the MEDLINE, EMBASE, and arXiv electronic databases was conducted for studies that compared CAD for prostate cancer detection or classification on MRI against radiologist interpretation and a histopathological reference standard, in treatment-naïve men with a clinical suspicion of prostate cancer. Twenty-seven studies were included in the final analysis. Due to substantial heterogeneities in the included studies, a narrative synthesis is presented. Several studies reported superior diagnostic accuracy for CAD over radiologist interpretation on small, internal patient datasets, though this was not observed in the few studies that performed evaluation using external patient data. Our review found insufficient evidence to suggest the clinical deployment of artificial intelligence algorithms at present. Further work is needed to develop and enforce methodological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.

Keywords: PRISMA-DTA; QUADAS-2; artificial intelligence; computer-aided diagnosis; deep learning; machine learning; magnetic resonance imaging; prostate cancer; systematic review.

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Conflict of interest statement

S.M. receives funding from National Cancer Imaging Translation Accelerator (NCITA), National Institute for Health Research (NIHR) and the University College London/ University College London Hospital (UCL/UCLH) Biomedical Research Centre. D.A. receives research support from Philips and Siemens. S.P. received funding from the University College London/ University College London Hospital (UCL/UCLH) Biomedical Research Centre.

Figures

Figure 1
Figure 1
PRISMA flow diagram of the systematic search.
Figure 2
Figure 2
Workflow of typical prostate cancer AI CAD systems. Systems are categorized as ROI Classification (ROI-C), Lesion Localization and Classification (LL&C), or Patient Classification (PAT-C). Blue indicates mpMRI input, yellow indicates manual processes, white indicates automated processes, and green indicates intermediate or final outputs. ROI = region of interest. CNN = convolutional neural network. ML = machine learning. ML* here refers to ML algorithms exclusive of CNNs, such as support vector machines, random forest, logistic regression, and artificial neural networks.
Figure 3
Figure 3
QUADAS-2 risk of bias and applicability concerns summary for all included studies.
See this image and copyright information in PMC

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