Evidence map›Paper›PMID 33991226›Full record

ArticleEuropean radiology2021

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging.

Tobias Penzkofer, Anwar R Padhani, Baris Turkbey, Masoom A Haider, Henkjan Huisman, Jochen Walz, Georg Salomon, Ivo G Schoots, Jonathan Richenberg, Geert Villeirs and 4 more

Abstract readConsensus Statement
In one paragraph

Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed
–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

41 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Tobias Penzkofer *Department of Radiology, Charité University Hospital, Augustenburger Platz 1, 13354, Berlin, Germany. tobias.penzkofer@charite.de.ORCID http://orcid.org/0000-0001-9591-8575
Anwar R Padhani *Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, UK.
Baris TurkbeyMolecular Imaging Branch, National Cancer Institute, NIH, Bethesda, MD, USA.
Masoom A HaiderJoint Department of Medical Imaging, Sinai Health System, University Health Network, University of Toronto, Toronto, Canada.
Henkjan HuismanDepartment of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.
Jochen WalzDepartment of Urology, Institut Paoli-Calmettes Cancer Centre, Marseille, France.
Georg SalomonMartini-Klinik am UKE, University Hospital Hamburg, Hamburg, Germany.
Ivo G SchootsRadiology & Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands.
Jonathan RichenbergDepartment of Imaging, BSUH NHS Trust, Brighton, UK.
Geert VilleirsDepartment of Radiology and Nuclear Medicine, Ghent University Hospital, Ghent, Belgium.
Valeria PanebiancoDepartment of Radiological Sciences, Oncology and Pathology, Sapienza/Policlinico Umberto I, Rome, Italy.
Olivier RouviereDepartment of Urinary and Vascular Imaging, Hospices Civils de Lyon, Lyon, France.
Vibeke Berg LogagerRadiological Department, Copenhagen University Hospital in Herlev-Gentofte, Herlev, Denmark.
Jelle BarentszDepartment of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands.

Funding

Imaging, image guided biopsy and treatment of prostate cancerZIABC012062 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI TÜRKBEY, BARIS · 2021 to 2025
$1.3M
Berlin Institute of Health Clinician Scientist Program
6 · The paper itself

Abstract

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined.

Indexed as

Artificial IntelligenceProstatic NeoplasmsHumansImage-Guided BiopsyMagnetic Resonance ImagingMaleArtificial intelligenceDeep learningImage-guided biopsyMultiparametric magnetic resonance imagingProstate cancer

Identifiers

PMID33991226
PMCPMC8589789

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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.