Syer, T;
Mehta, P;
Antonelli, M;
Mallett, S;
Atkinson, D;
Ourselin, S;
Punwani, S;
(2021)
Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: A systematic review and recommendations for future studies.
Cancers
, 13
(13)
, Article 3318. 10.3390/cancers13133318.
Preview |
Text
cancers-13-03318.pdf - Published Version Download (1MB) | Preview |
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 method-ological standards, promote access to large diverse datasets, and conduct prospective evaluations before clinical adoption can be considered.
| Type: | Article |
|---|---|
| Title: | Artificial intelligence compared to radiologists for the initial diagnosis of prostate cancer on magnetic resonance imaging: A systematic review and recommendations for future studies |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/cancers13133318 |
| Publisher version: | https://doi.org/10.3390/ cancers13133318 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | artificial intelligence; computer-aided diagnosis; machine learning; deep learning; magnetic resonance imaging; PRISMA-DTA; prostate cancer; QUADAS-2; systematic review |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Department of Imaging UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Medicine > Experimental and Translational Medicine UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10130755 |
Archive Staff Only
 |
View Item |
