Evidence map›Paper›PMID 40506239›Full record

ArticleJournal of nuclear medicine : official publication, Society of Nuclear Medicine2025

Summary Report of the SNMMI AI Task Force Radiomics Challenge 2024.

Ronald Boellaard, Arman Rahmim, Jacoba J Eertink, Ulrich Duehrsen, Lars Kurch, Pieternella J Lugtenburg, Sanne E Wiegers, Gerben J C Zwezerijnen, Josée M Zijlstra, Martijn W Heymans and 1 more

Abstract readEditorial
In one paragraph

Article in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2025. 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. Neuroimaging in Tremor.Seminars in neurology · 2026
    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

11 authors.

Ronald BoellaardDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands; r.boellaard@amsterdamumc.nl.
Arman RahmimDepartments of Radiology and Physics, University of British Columbia, Vancouver, British Columbia, Canada.
Jacoba J EertinkDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands.
Ulrich DuehrsenDepartment of Hematology, West German Cancer Center, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
Lars KurchClinic and Polyclinic for Nuclear Medicine, Department of Nuclear Medicine, University of Leipzig, Leipzig, Germany.
Pieternella J LugtenburgDepartment of Hematology, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Sanne E WiegersDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands.
Gerben J C ZwezerijnenDepartment of Radiology and Nuclear Medicine, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands.
Josée M ZijlstraDepartment of Hematology, Amsterdam UMC, Cancer Center Amsterdam, Amsterdam, The Netherlands.
Martijn W HeymansDepartment of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, Amsterdam, The Netherlands; and.
Irène BuvatInstitut Curie, Université PSL, Laboratoire d'Imagerie Translationnelle en Oncologie, Orsay, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In medical imaging, challenges are competitions that aim to provide a fair comparison of different methodologic solutions to a common problem. Challenges typically focus on addressing real-world problems, such as segmentation, detection, and prediction tasks, using various types of medical images and associated data. Here, we describe the organization and results of such a challenge to compare machine-learning models for predicting survival in patients with diffuse large B-cell lymphoma using a baseline

Indexed as

Advisory CommitteesLymphoma, Large B-Cell, DiffuseNuclear MedicinePositron Emission Tomography Computed TomographyFluorodeoxyglucose F18HumansImage Processing, Computer-AssistedMachine LearningRadiomicsFluorodeoxyglucose F18lymphomamachine learningPETprognosisradiomics

Identifiers

PMID40506239
PMCPMC12320580

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.