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Comparative Performance of Fully-Automated and Semi-Automated Artificial Intelligence Methods for the Detection of Clinically Significant Prostate Cancer on MRI: a Systematic Review

Published version
Peer-reviewed

Type

Article

Change log

Authors

Sushentsev, Nikita 
Moreira Da Silva, Nadia 
Yeung, Michael 

Abstract

Abstract Objectives We systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automated traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa) and benign conditions. Methods We performed a computerised bibliographic search of studies indexed in MEDLINE/PubMed, arXiv, medRxiv, and bioRxiv between 1 January 2016 and 31 July 2021. Two reviewers performed the title/abstract and full-text screening. The remaining papers were screened by four reviewers using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) for DL studies and Radiomics Quality Score (RQS) for TML studies. Papers that fulfilled the pre-defined screening requirements underwent full CLAIM/RQS evaluation alongside the risk of bias assessment using QUADAS-2, both conducted by the same four reviewers. Standard measures of discrimination were extracted for the developed predictive models. Results 17/28 papers (five DL and twelve TML) passed the quality screening and were subject to a full CLAIM/RQS/QUADAS-2 assessment, which revealed a substantial study heterogeneity that precluded us from performing quantitative analysis as part of this review. The mean RQS of TML papers was 11/36, and a total of five papers had a high risk of bias. AUCs of DL and TML papers with low risk of bias ranged between 0.80-0.89 and 0.75-0.88, respectively. Conclusion We observed comparable performance of the two classes of AI methods and identified a number of common methodological limitations and biases that future studies will need to address to ensure the generalisability of the developed models.

Description

Funder: Cancer Research UK


Funder: CRUK National Cancer Imaging Translational Accelerator


Funder: Cambridge Experimental Cancer Medicine Centre


Funder: The Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre


Funder: National Institute of Health Research Cambridge Biomedical Research Centre


Funder: Wellcome Trust


Funder: Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester


Funder: Wellcome Trust Innovator Award

Keywords

Artificial intelligence, Deep learning, MRI, Machine learning, Prostate cancer

Journal Title

Insights into Imaging

Conference Name

Journal ISSN

1869-4101
1869-4101

Volume Title

13

Publisher

Springer
Sponsorship
Cancer Research UK (C96/A25177)
National Institute for Health and Care Research (IS-BRC-1215-20014)
Wellcome Trust (215733/Z/19/Z)
The review is supported by the National Institute of Health Research Cambridge Biomedical Research Centre, Cancer Research UK (Cambridge Imaging Centre grant number C197/A16465), the Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, Gates Cambridge Trust, The Mark Foundation for Cancer Research, the Wellcome Trust, CRUK National Cancer Imaging Translational Accelerator (NCITA) and the Cambridge Experimental Cancer Medicine Centre