Evidence map›Paper›PMID 27484386›Full record

ArticleThe Journal of urology2017

Magnetic Resonance Imaging Underestimation of Prostate Cancer Geometry: Use of Patient Specific Molds to Correlate Images with Whole Mount Pathology.

Alan Priester, Shyam Natarajan, Pooria Khoshnoodi, Daniel J Margolis, Steven S Raman, Robert E Reiter, Jiaoti Huang, Warren Grundfest, Leonard S Marks

Abstract read
In one paragraph

Article in The Journal of urology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 137 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
137citing papers in PubMed, 2 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

137 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. Head-to-Head Comparison ofJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022
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  19. Comparison of 7-teslaProstate international · 2025
    Article
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77 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Alan PriesterDepartment of Bioengineering, University of California Los Angeles, Los Angeles, California.
Shyam NatarajanDepartment of Urology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California; Department of Bioengineering, University of California Los Angeles, Los Angeles, California.
Pooria KhoshnoodiDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California.
Daniel J MargolisDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California.
Steven S RamanDepartment of Radiological Sciences, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California.
Robert E ReiterDepartment of Urology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California.
Jiaoti HuangDepartment of Pathology, Duke University School of Medicine, Durham, North Carolina.
Warren GrundfestDepartment of Bioengineering, University of California Los Angeles, Los Angeles, California.
Leonard S MarksDepartment of Urology, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, California. Electronic address: lmarks@mednet.ucla.edu.

Funding

UCLA SPORE IN PROSTATE CANCERP50CA092131 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FREEMAN, MICHAEL R · 2002 to 2023
$42.6M
Biopsy Tracking and MRI Fusion to Enhance Imaging of Cancer Within the ProstateR01CA158627 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI MARKS, LEONARD STEPHEN · 2011 to 2013
$1.4M
NCI NIH HHS P50 CA092131NCI NIH HHS R01 CA158627
6 · The paper itself

Abstract

purposeWe evaluated the accuracy of magnetic resonance imaging in determining the size and shape of localized prostate cancer. MATERIALS AND

methodsThe subjects were 114 men who underwent multiparametric magnetic resonance imaging before radical prostatectomy with patient specific mold processing of the specimen from 2013 to 2015. T2-weighted images were used to contour the prostate capsule and cancer suspicious regions of interest. The contours were used to design and print 3-dimensional custom molds, which permitted alignment of excised prostates with magnetic resonance imaging scans. Tumors were reconstructed in 3 dimensions from digitized whole mount sections. Tumors were then matched with regions of interest and the relative geometries were compared.

resultsOf the 222 tumors evident on whole mount sections 118 had been identified on magnetic resonance imaging. For the 118 regions of interest mean volume was 0.8 cc and the longest 3-dimensional diameter was 17 mm. However, for matched pathological tumors, of which most were Gleason score 3 + 4 or greater, mean volume was 2.5 cc and the longest 3-dimensional diameter was 28 mm. The median tumor had a 13.5 mm maximal extent beyond the magnetic resonance imaging contour and 80% of cancer volume from matched tumors was outside region of interest boundaries. Size estimation was most accurate in the axial plane and least accurate along the base-apex axis.

conclusionsMagnetic resonance imaging consistently underestimates the size and extent of prostate tumors. Prostate cancer foci had an average diameter 11 mm longer and a volume 3 times greater than T2-weighted magnetic resonance imaging segmentations. These results may have important implications for the assessment and treatment of prostate cancer.

Indexed as

AgedHumansImaging, Three-DimensionalMagnetic Resonance ImagingMaleMiddle AgedModels, AnatomicProstateProstatectomyProstatic NeoplasmsRetrospective Studiescomputer-assisteddiagnostic errorsimage processingmagnetic resonance imagingpathologyprostatic neoplasmssurgical

Identifiers

PMID27484386
PMCPMC5540646

What OpenQuestion holds

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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.