Evidence map›Paper›PMID 31187216›Full record

ArticleEuropean radiology2019

Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

Michela Antonelli, Edward W Johnston, Nikolaos Dikaios, King K Cheung, Harbir S Sidhu, Mrishta B Appayya, Francesco Giganti, Lucy A M Simmons, Alex Freeman, Clare Allen and 4 more

Erratum issuedOpen access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 43 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 4 pooled it
8.4field-weighted citation impact, top 2% of its field
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

43 citing papers in PubMed, 4 syntheses or guidelines pooled it, 78 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 3 institutions in 1 country.

Michela AntonelliCentre for Medical Image Computing, University College London, London, UK.
Edward W JohnstonCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Nikolaos DikaiosCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
King K CheungCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Harbir S SidhuCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Mrishta B AppayyaCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Francesco GigantiDepartment of Radiology, University College London Hospital, London, UK.
Lucy A M SimmonsDivision of Surgery and Interventional Science, University College London, London, UK.
Alex FreemanDepartment of Pathology, University College London Hospital, London, UK.
Clare AllenDepartment of Radiology, University College London Hospital, London, UK.
Hashim U AhmedDivision of Surgery and Interventional Science, University College London, London, UK.
David AtkinsonCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Sebastien OurselinSchool of Biomedical Engineering and Imaging Science, King's College London, London, UK.
Shonit PunwaniCentre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK. shonit.punwani@gmail.com.ORCID http://orcid.org/0000-0001-9865-0260
University College London · GBUniversity College Hospital · GBKing's College London · GB

Funding

Cancer Research UK 26854Cancer Research UK 27066
6 · The paper itself

Abstract

objectiveThe purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.

methodsA retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists.

resultsThe best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82).

conclusionsMachine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. KEY POINTS: • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.

Indexed as

Machine LearningArea Under CurveBiopsyClinical CompetenceDiffusion Magnetic Resonance ImagingHumansImage Interpretation, Computer-AssistedMaleMiddle AgedNeoplasm GradingProstatic NeoplasmsRadiologistsRetrospective StudiesSensitivity and SpecificityDiagnosis, computer-assistedGleason scoreMachine learningMagnetic resonance imagingProstate cancer

Identifiers

PMID31187216
PMCPMC6682575
OpenAlexW2951787500

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.