Comparative Performance of Fully-Automated and Semi-Automated Artificial Intelligence Methods for the Detection of Clinically Significant Prostate Cancer on MRI: a Systematic Review
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Authors
Sushentsev, Nikita
Moreira Da Silva, Nadia
Yeung, Michael
Rundo, Leonardo
Publication Date
2022-03-28Journal Title
Insights into Imaging
ISSN
1869-4101
Publisher
Springer
Volume
13
Issue
1
Language
eng
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Sushentsev, N., Moreira Da Silva, N., Yeung, M., Barrett, T., Sala, E., Roberts, M., & Rundo, L. (2022). Comparative Performance of Fully-Automated and Semi-Automated Artificial Intelligence Methods for the Detection of Clinically Significant Prostate Cancer on MRI: a Systematic Review. Insights into Imaging, 13 (1) https://doi.org/10.1186/s13244-022-01199-3
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
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.
Keywords
Artificial intelligence, MRI, prostate cancer, Machine Learning, Deep Learning
Sponsorship
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
Funder references
Cancer Research UK (C96/A25177)
National Institute for Health Research (IS-BRC-1215-20014)
Wellcome Trust (215733/Z/19/Z)
Identifiers
35347462, PMC8960511
External DOI: https://doi.org/10.1186/s13244-022-01199-3
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336643
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