Evidence map›Paper›PMID 36809425›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2023

The role of [

Adriano Basso Dias, Seyed Ali Mirshahvalad, Claudia Ortega, Nathan Perlis, Alejandro Berlin, Theodorus van der Kwast, Sangeet Ghai, Kartik Jhaveri, Ur Metser, Masoom Haider and 2 more

Abstract readComment
PubMed Publisher
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Trial
  2. An interpretableEuropean journal of nuclear medicine and molecular imaging · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Article
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  8. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Adriano Basso Dias *Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada. Adriano.BassoDias@uhn.ca.ORCID 0000-0003-4761-8708
Seyed Ali Mirshahvalad *Joint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.ORCID 0000-0003-2271-9764
Claudia OrtegaJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Nathan PerlisDivision of Urology, Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Alejandro BerlinDepartment of Radiation Oncology, Princess Margaret Cancer Center, University Health Network & University of Toronto, Toronto, ON, Canada.
Theodorus van der KwastLaboratory Medicine Program, University Health Network, Toronto, ON, Canada.
Sangeet GhaiJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Kartik JhaveriJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Ur MetserJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Masoom HaiderJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.
Lisa AveryDepartment of Biostatistics, Princess Margaret Cancer Centre, Toronto, ON, Canada.
Patrick Veit-HaibachJoint Department of Medical Imaging, University Medical Imaging Toronto (UMIT), University Health Network, Mount Sinai Hospital & Women's College Hospital; University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the diagnostic accuracy of [

methodsPatients with confirmed or suspected PCa, who underwent [

resultsAll radiomic models outperformed the clinical models. The best model for grade group prediction was the combination of PET + ADC + T2w radiomic features, showing sensitivity, specificity, accuracy, and AUC of 0.85, 0.83, 0.84, and 0.85, respectively. The MRI-derived (ADC + T2w) features showed sensitivity, specificity, accuracy, and AUC of 0.88, 0.78, 0.83, and 0.84, respectively. PET-derived features showed 0.83, 0.68, 0.76, and 0.79, respectively. The baseline clinical model showed 0.73, 0.44, 0.60, and 0.58, respectively. The addition of the clinical model to the best radiomic model did not improve the diagnostic performance. The performances of MRI and PET/MRI radiomic models as per the cross-validation scheme yielded an accuracy of 0.80 (AUC = 0.79), whereas clinical models presented an accuracy of 0.60 (AUC = 0.60).

conclusionThe combined [

Indexed as

Prostatic NeoplasmsHumansMagnetic Resonance ImagingMalePositron-Emission TomographyReproducibility of ResultsRetrospective StudiesPET/MRIProstateRadiomics

Identifiers

PMID36809425

What OpenQuestion holds

Textmetadata
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.