Evidence map›Paper›PMID 41283236›Full record

ArticleHematology reports2025

AI Improves Agreement and Reduces Time for Quantifying Metabolic Tumour Burden in Hodgkin Lymphoma.

May Sadik, Sally F Barrington, Johannes Ulén, Olof Enqvist, Elin Trägårdh, Babak Saboury, Anne Lerberg Nielsen, Annika Loft, Jose Luis Loaiza Gongora, Jesus Lopez Urdaneta and 3 more

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Article in Hematology reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

May SadikDepartment of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden.
Sally F BarringtonClinical PET Centre, School of Biomedical Engineering and Imaging Sciences Kings College, London SE5 9RS, UK.
Johannes UlénEigenvision AB, 211 30 Malmö, Sweden.
Olof EnqvistEigenvision AB, 211 30 Malmö, Sweden.
Elin TrägårdhClinical Physiology and Nuclear Medicine, Lund University and Skåne University Hospital, 205 02 Malmö, Sweden.ORCID 0000-0002-7116-303X
Babak SabouryDepartment of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD 20892, USA.ORCID 0000-0002-8632-6292
Anne Lerberg NielsenDepartment of Nuclear Medicine, Odense University Hospital, 5000 Odense, Denmark.
Annika LoftDepartment of Clinical Physiology, Nuclear Medicine and PET, Centre of Diagnostic Investigations, Rigshospitalet, University of Copenhagen, 2100 Copenhagen, Denmark.
Jose Luis Loaiza GongoraDepartment of Diagnostic Imaging, Akershus University Hospital, 1478 Oslo, Norway.
Jesus Lopez UrdanetaDepartment of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden.
Rajender KumarDepartment of Nuclear Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh 160012, India.
Martijn van EssenDepartment of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden.
Lars EdenbrandtDepartment of Molecular and Clinical Medicine, Clinical Physiology, Sahlgrenska University Hospital, Sahlgrenska Academy at the University of Gothenburg, 413 90 Gothenburg, Sweden.ORCID 0000-0002-0263-8820

Funding

SFB acknowledges support from the National Institute for Health and Care Research (NIHR) [RP-2-16-07-001]. King's College London and UCL Comprehensive Cancer Imaging Centre is funded by the CRUK and EPSRC in association with the MRC and Department of Heal [RP-2-16-07-001]the Swedish State under the agreement between the Swedish Government and the Country Councils; the ALF agreement 70380the Wellcome/EPSRC Centre for Medical Engineering at King's College London [WT203148/Z/16/Z] and the National Institute for Health and Care Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College Lo [WT203148/Z/16/Z]
6 · The paper itself

Abstract

backgroundThe aim was to evaluate whether an artificial intelligence (AI)-based tool for the automated quantification of the total metabolic tumour volume (tMTV) in patients with Hodgkin lymphoma (HL) could support nuclear medicine specialists in lesion segmentation and thereby enhance inter-observer agreement.

methodsForty-eight consecutive patients who underwent staging with [18F]FDG PET/CT were included. Eight invited specialists from different hospitals were asked to manually segment lesions for tMTV calculations in 12 cases without AI advice, and to use automated AI segmentation in a further 12 cases, with editing as required, i.e., segmenting/adjusting 24 cases each. Each case was segmented by two specialists manually and by two different specialists using the AI tool, allowing for the pairwise comparison of inter-observer variability.

resultsThe median difference between two specialists performing manual tMTV segmentations was 26 cm

conclusionsAn automated AI-based tool can significantly increase agreement among specialists quantifying tMTV in HL patients staged with [18F]FDG PET/CT, without markedly changing the measurements.

Indexed as

artificial intelligenceFluorodeoxyglucose F18Hodgkin diseaseobserver variationtotal metabolic tumour volume

Identifiers

PMID41283236
PMCPMC12641687

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