Evidence map›Paper›PMID 29651763›Full record

ArticleEuropean radiology2018

Computer-aided diagnosis prior to conventional interpretation of prostate mpMRI: an international multi-reader study.

Matthew D Greer, Nathan Lay, Joanna H Shih, Tristan Barrett, Leonardo Kayat Bittencourt, Samuel Borofsky, Ismail Kabakus, Yan Mee Law, Jamie Marko, Haytham Shebel and 7 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 1 pooled it
10.1field-weighted citation impact, top 1% 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

49 citing papers in PubMed, 1 synthesis or guideline pooled it, 92 citations in OpenAlex.

  1. Pooled it
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  15. PI-RADS: Where Next?Radiology · 2023
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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

17 authors at 9 institutions in 6 countries.

Matthew D GreerMolecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
Nathan LayImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Joanna H ShihBiometric Research Program, NCI, NIH, Bethesda, MD, USA.
Tristan BarrettDepartment of Radiology, University of Cambridge School of Medicine, Cambridge, UK.
Leonardo Kayat BittencourtUniversidade Federal Fluminense and CDPI Clinics/DASA, Rio de Janeiro, RJ, Brazil.
Samuel BorofskyGeorge Washington University Hospital, Washington, DC, USA.
Ismail KabakusHacettepe University, Ankara, Turkey.
Yan Mee LawSingapore General Hospital, Singapore, Singapore.
Jamie MarkoRadiology and Imaging Sciences Department, Clinical Center, NIH, Bethesda, MD, USA.
Haytham ShebelDepartment of Radiology, Nephrology Center, Mansoura University, Mansoura, 35516, Egypt.
Francesca V MertanMolecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
Maria J MerinoLaboratory of Pathology, NCI, NIH, Bethesda, MD, USA.
Bradford J WoodCenter for Interventional Oncology, NCI and Radiology Imaging Sciences, Clinical Center, NIH, Bethesda, MD, USA.
Peter A PintoUrologic Oncology Branch, NCI, NIH, Bethesda, MD, USA.
Ronald M SummersImaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA.
Peter L ChoykeMolecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA.
Baris TurkbeyMolecular Imaging Program, NCI, NIH, 10 Center Drive, Room B3B85, Bethesda, MD, 20892, USA. turkbeyi@mail.nih.gov.
National Institutes of Health Clinical Center · USNational Institutes of Health · USGeorge Washington University Hospital · USHacettepe University · TRMansoura University · EGSingapore General Hospital · SGUniversidade Federal Fluminense · BRUniversity of Cambridge · GBZimmer Biomet (United States) · US

Funding

Prostate Cancer ImagingZIABC010655 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CHOYKE, PETER L · 2009 to 2025
$49.2M
Center for Interventional OncologyZIDBC011242 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2009 to 2025
$18.7M
Prostate Cancer ImagingZ01BC010655 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CHOYKE, PETER L · 2005 to 2008
$2.1M
Computer Aided Detection for Radiologic ImagesZIACL040004 · CLC · CLINICAL CENTER · PI SUMMERS, RONALD M. · 2009 to 2025
$0k
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Cancer Research UK 16465Intramural NIH HHS Z01 BC010655Intramural NIH HHS ZIA BC010655Intramural NIH HHS ZIA CL040015Intramural NIH HHS ZID BC011242
6 · The paper itself

Abstract

objectivesTo evaluate if computer-aided diagnosis (CAD) prior to prostate multi-parametric MRI (mpMRI) can improve sensitivity and agreement between radiologists.

methodsNine radiologists (three each high, intermediate, low experience) from eight institutions participated. A total of 163 patients with 3-T mpMRI from 4/2012 to 6/2015 were included: 110 cancer patients with prostatectomy after mpMRI, 53 patients with no lesions on mpMRI and negative TRUS-guided biopsy. Readers were blinded to all outcomes and detected lesions per PI-RADSv2 on mpMRI. After 5 weeks, readers re-evaluated patients using CAD to detect lesions. Prostatectomy specimens registered to MRI were ground truth with index lesions defined on pathology. Sensitivity, specificity and agreement were calculated per patient, lesion level and zone-peripheral (PZ) and transition (TZ).

resultsIndex lesion sensitivity was 78.2% for mpMRI alone and 86.3% for CAD-assisted mpMRI (p = 0.013). Sensitivity was comparable for TZ lesions (78.7% vs 78.1%; p = 0.929); CAD improved PZ lesion sensitivity (84% vs 94%; p = 0.003). Improved sensitivity came from lesions scored PI-RADS < 3 as index lesion sensitivity was comparable at PI-RADS ≥ 3 (77.6% vs 78.1%; p = 0.859). Per patient specificity was 57.1% for CAD and 70.4% for mpMRI (p = 0.003). CAD improved agreement between all readers (56.9% vs 71.8%; p < 0.001).

conclusionsCAD-assisted mpMRI improved sensitivity and agreement, but decreased specificity, between radiologists of varying experience. KEY POINTS: • Computer-aided diagnosis (CAD) assists clinicians in detecting prostate cancer on MRI. • CAD assistance improves agreement between radiologists in detecting prostate cancer lesions. • However, this CAD system induces more false positives, particularly for less-experienced clinicians and in the transition zone. • CAD assists radiologists in detecting cancer missed on MRI, suggesting a path for improved diagnostic confidence.

Indexed as

Image Interpretation, Computer-AssistedAgedHumansMagnetic Resonance ImagingMaleMiddle AgedProstatectomyProstatic NeoplasmsRetrospective StudiesSensitivity and Specificitycomputer assistedComputer-assisted diagnosisImage interpretationMRI scansProstate cancer

Identifiers

PMID29651763
PMCPMC8023433
OpenAlexW2796711585

What OpenQuestion holds

Textmetadata
LicenceTDM
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.