Evidence map›Paper›PMID 39510993›Full record

ArticleEJNMMI reports2024

Enhancing the diagnostic capacity of [

Philip Alexander Glemser, Martin Freitag, Balint Kovacs, Nils Netzer, Antonia Dimitrakopoulou-Strauss, Uwe Haberkorn, Klaus Maier-Hein, Constantin Schwab, Stefan Duensing, Bettina Beuthien-Baumann and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

Philip Alexander Glemser *Department of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Martin Freitag *Department of Nuclear Medicine, Faculty of Medicine, Medical Center-University of Freiburg, University of Freiburg, Freiburg, Germany.
Balint KovacsDivision of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
Nils NetzerDepartment of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Antonia Dimitrakopoulou-StraussClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69210, Heidelberg, Germany.
Uwe HaberkornDepartment of Nuclear Medicine, University Hospital Heidelberg, Heidelberg, Germany.
Klaus Maier-HeinDivision of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.
Constantin SchwabInstitute of Pathology, Heidelberg University Hospital, Heidelberg, Germany.
Stefan DuensingDepartment of Urology, University Hospital Heidelberg, Heidelberg, Germany.
Bettina Beuthien-BaumannDepartment of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Heinz-Peter SchlemmerDepartment of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
David BonekampDepartment of Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Frederik GieselDepartment of Nuclear Medicine, Medical Faculty, University Hospital Duesseldorf, Düsseldorf, Germany.
Christos SachpekidisClinical Cooperation Unit Nuclear Medicine, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, D-69210, Heidelberg, Germany. christos_saxpe@yahoo.gr.ORCID http://orcid.org/0000-0001-8739-8741

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [

resultsA total of seven patients underwent whole-body [

conclusionFirstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

Indexed as

18F-PSMA-1007AIDCEPET/MRIPrimary stagingProstate cancer

Identifiers

PMID39510993
PMCPMC11543981

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.