Evidence map›Paper›PMID 35347462›Full record

ReviewInsights into imaging2022

Comparative performance of fully-automated and semi-automated artificial intelligence methods for the detection of clinically significant prostate cancer on MRI: a systematic review.

Nikita Sushentsev, Nadia Moreira Da Silva, Michael Yeung, Tristan Barrett, Evis Sala, Michael Roberts, Leonardo Rundo

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  4. AI-accelerated prostate MRI: a systematic review.The British journal of radiology · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Nikita SushentsevDepartment of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK. ns784@medschl.cam.ac.uk.ORCID http://orcid.org/0000-0003-4500-9714
Nadia Moreira Da SilvaLucida Medical Ltd, Biomedical Innovation Hub, University of Cambridge, Cambridge, UK.
Michael YeungDepartment of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
Tristan BarrettDepartment of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
Evis SalaDepartment of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.
Michael Roberts *Department of Applied Mathematics and Theoretical Physics, The Cambridge Mathematics of Information in Healthcare Hub, University of Cambridge, Cambridge, UK.
Leonardo Rundo *Department of Radiology, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital and University of Cambridge, Cambridge Biomedical Campus, Box 218, Cambridge, CB2 0QQ, UK.

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectivesWe 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.

methodsWe 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.

results17/28 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.

conclusionWe 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.

Indexed as

Artificial intelligenceDeep learningMachine learningMRIProstate cancer

Identifiers

PMID35347462
PMCPMC8960511

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.