Evidence map›Paper›PMID 40993940›Full record

ArticleMedical physics2025

Comparison of organ volumes and standardized uptake values in [18F]FDG-PET/CT images using MOOSE and TotalSegmentator to segment CT images.

Julie Auriac, Christophe Nioche, Narinée Hovhannisyan-Baghdasarian, Charlotte Loisel, Romain-David Seban, Nina Jehanno, Lalith Kumar Shiyam Sundar, Thomas Beyer, Irène Buvat, Fanny Orlhac

Abstract readComparative Study
In one paragraph

Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers 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

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Julie AuriacLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Christophe NiocheLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Narinée Hovhannisyan-BaghdasarianLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Charlotte LoiselLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Romain-David SebanLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Nina JehannoLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Lalith Kumar Shiyam SundarQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.
Thomas BeyerQuantitative Imaging and Medical Physics (QIMP) Team, Medical University of Vienna, Vienna, Austria.
Irène BuvatLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.
Fanny OrlhacLITO U1288, Institut Curie, PSL University, Inserm, Orsay, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManual segmentation of organs from PET/CT images is a time-consuming and highly operator-dependent task. Open software solutions are now available to automatically segment all major anatomical structures in CT images. PURPOSE: We compared the volumes and standardized uptake values (SUV) extracted from [18F]FDG-PET/CT patient scans for 33 anatomical structures segmented using two deep learning (DL) algorithms to determine if they are interchangeable.

methodsBaseline [18F]FDG-PET/CT images were collected retrospectively for 315 women with metastatic breast cancer. A total of 33 anatomical volumes of interest (VOI) were segmented from the whole-body CT scans using both MOOSE v.3.0.14 and TotalSegmentator v.2.0.5 and copied onto the corresponding PET images. For each VOI, the volume from the CT image and SUVmax, SUVpeak and SUVmean from the PET image were extracted. The resulting values were compared using the relative difference for each feature.

resultsFollowing DL segmentation, resulting organ volumes differed by less than 10% for 19/33 organs in more than 80% (252/315) of patients. Four organs were segmented with volume differences greater than 20% in 1/5th of patients: bladder (48%, p < 0.0001), portal and splenic veins (34%, p < 0.0001), thyroid (16%, p < 0.0001), adrenal glands (15%, p < 0.0001). SUVmax and SUVpeak were affected by the choice of DL algorithms, with values differing by less than 10% in more than 80% of patients for only 16 and 19 out of 33 organs, respectively. In contrast, SUVmean was less affected with differences of less than 10% in more than 80% of patients for all anatomical structures, except the bladder, lungs and skull.

conclusionsThe two software tools produce similar results in volume estimates for most anatomical structures. SUVmean is less dependent on the segmentation algorithm than SUVmax and SUVpeak and shows excellent reproducibility for all anatomical structures studied except for the bladder, the lungs and the skull.

Indexed as

Fluorodeoxyglucose F18Image Processing, Computer-AssistedPositron Emission Tomography Computed TomographySoftwareAlgorithmsBiological TransportBreast NeoplasmsDeep LearningFemaleHumansOrgan SizeReference StandardsRetrospective StudiesFluorodeoxyglucose F18PET/CTradiomicssegmentation

Identifiers

PMID40993940
PMCPMC12460933

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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.