Evidence map›Paper›PMID 30013306›Full record

ArticleCancer informatics2018

A Supervised Learning Tool for Prostate Cancer Foci Detection and Aggressiveness Identification using Multiparametric magnetic resonance imaging/magnetic resonance spectroscopy imaging.

Gokhan Kirlik, Rao Gullapalli, Warren D'Souza, Gazi Md Daud Iqbal, Michael Naslund, Jade Wong, John C Papadimitriou, John Papadimitrou, Steve Roys, Nilesh Mistry and 1 more

Erratum issuedAbstract read
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In one paragraph

Article in Cancer informatics, 2018. 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

  • Erratum issued
    Corrigendum.2018 · Papadimitrou J [corrected to Papadimitriou JC]
5 · Who and what money

Authors and funding

11 authors.

Gokhan KirlikDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
Rao GullapalliDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Warren D'SouzaDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
Gazi Md Daud IqbalDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.
Michael NaslundDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Jade WongDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
John C Papadimitriou
John Papadimitrou *Department of Pathology, University of Maryland School of Medicine, Baltimore, MD, USA.
Steve RoysDepartment of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Nilesh MistrySiemens Healthcare, Raleigh-Durham, NC, USA.
Hao ZhangDepartment of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer is the most frequently diagnosed cancer in men in the United States. The current main methods for diagnosing prostate cancer include prostate-specific antigen test and transrectal biopsy. Prostate-specific antigen screening has been criticized for overdiagnosis and unnecessary treatment, and transrectal biopsy is an invasive procedure with low sensitivity for diagnosis. We provided a quantitative tool using supervised learning with multiparametric imaging to be able to accurately detect cancer foci and its aggressiveness. A total of 223 specimens from patients who received magnetic resonance imaging (MRI) and magnetic resonance spectroscopy imaging prior to the surgery were studied. Multiparametric imaging included extracting T2-map, apparent diffusion coefficient (ADC) using diffusion-weighted MRI,

Indexed as

diagnostic imagingmultiparametric MRI/MRSIpredictive modelingprostate cancer

Identifiers

PMID30013306
PMCPMC6043929

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.