Evidence map›Paper›PMID 41686074›Full record

ArticleRadiology. Imaging cancer2026

Evaluating Performance of a Deep Learning-based Artificial Intelligence Model for Ovarian Tumor Classification Using a Multicenter CT Dataset.

Anna H Koch, Cris H B Claessens, Ingrid Nies, Eloy W R Schultz, Terese A E Hellström, Tim Boers, Eveline M L Dekker, Caroline L P Muntinga, Janneke S Hoogstad-van Evert, Ilse Niers-Stobbe and 5 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Radiology. Imaging cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

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

15 authors.

Anna H KochDepartment of Obstetrics and Gynecology and Catharina Cancer Institute, Catharina Hospital, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.ORCID 0000-0003-4947-3514
Cris H B ClaessensDepartment of Medical Imaging, Eindhoven University of Technology, Eindhoven, the Netherlands.
Ingrid NiesDepartment of Obstetrics and Gynecology and Catharina Cancer Institute, Catharina Hospital, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.
Eloy W R SchultzDepartment of Medical Imaging, Eindhoven University of Technology, Eindhoven, the Netherlands.
Terese A E HellströmDepartment of Medical Imaging, Eindhoven University of Technology, Eindhoven, the Netherlands.
Tim BoersDepartment of Medical Imaging, Eindhoven University of Technology, Eindhoven, the Netherlands.ORCID 0000-0003-1944-8908
Eveline M L DekkerDepartment of Obstetrics and Gynecology and Catharina Cancer Institute, Catharina Hospital, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.
Caroline L P MuntingaDepartment of Obstetrics and Gynecology and Catharina Cancer Institute, Catharina Hospital, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.
Janneke S Hoogstad-van EvertDepartment of Obstetrics and Gynecology, Amphia Hospital, Breda, the Netherlands.
Ilse Niers-StobbeDepartment of Radiology, Amphia Hospital, Breda, the Netherlands.
Annemarie BruiningDepartment of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek, Amsterdam, the Netherlands.
Christianne A R LokDepartment of Gynecologic Oncology, Center of Gynecologic Oncology Amsterdam, Amsterdam, the Netherlands.
Joost NederendDepartment of Radiology and Catharina Cancer Institute, Catharina Hospital, Eindhoven, the Netherlands.ORCID 0000-0002-9905-5866
Fons van der SommenDepartment of Medical Imaging, Eindhoven University of Technology, Eindhoven, the Netherlands.ORCID 0000-0002-3593-2356
Jurgen M J PiekDepartment of Obstetrics and Gynecology and Catharina Cancer Institute, Catharina Hospital, Michelangelolaan 2, 5623 EJ Eindhoven, the Netherlands.ORCID 0000-0002-0487-0631

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose To develop a deep learning-based, computer-aided diagnosis (CADx) model for preoperative classification of ovarian tumors (OTs) on CT scans and to compare its performance with current US models and radiologist assessments. Materials and Methods This retrospective multicenter study (January 2021-November 2023) included patients with indeterminate OTs. The dataset comprised training, internal (

Indexed as

Artificial IntelligenceDeep LearningDiagnosis, Computer-AssistedOvarian NeoplasmsTomography, X-Ray ComputedAdultAgedFemaleHumansMiddle AgedRetrospective StudiesSensitivity and SpecificityComputer-aided DiagnosticsMulticenter TrialOvarian CancerOvarian Tumor Classification

Identifiers

PMID41686074
PMCPMC13036679

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.