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Validation of a Body-Composition Segmentation Software on a Diverse Public CT Scan Cohort

Validation of a Body-Composition Segmentation Software (Soma) on a Diverse Cohort of Publicly Available CT Scans

Who can join

As published by the study on ClinicalTrials.gov. "Not stated" means the study hasn't said — ask the study team.

Age
18 Years and older
Sex
All sexes
Healthy volunteers
Accepted
Compensation
Not stated

Other requirements are listed under Eligibility below. Only the study team can confirm whether you can join.

What to expect

No one can promise a trial will help you personally — that is what the research is trying to find out.

What participation involves

  • Procedures mentioned: CT scans

Only what the study's summary and detailed description on ClinicalTrials.gov say. Time periods may describe the whole study or a part of it — ask the study team what applies to you.

Show the study's own words
  • "This study evaluates the standalone performance of Soma, a deep-learning software developed by Nucleo Research, Inc. for the automated segmentation of body-composition tissues (skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, and intramuscular adipose tissue) on whole-body computed tomography (CT) images."
  • "A total of 200 CT scans are sampled by stratified design from a curated pool of 2,066 scans aggregated from six publicly available, de-identified imaging datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, RATIC)."
  • "Retrospective, multi-source observational validation on 200 de-identified abdominal CT scans, drawn by stratified sampling from a curated pool of 2,066 scans across six publicly available datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, RATIC)."
Compensation
Not stated
  • Study-related exams and procedures are typically provided at no cost. This is how many trials work in general — confirm the details for this study with the study team.
  • Participants are monitored closely by the research team throughout the study.
  • Some participants may receive access to an investigational treatment, which is still being studied.
  • Your participation helps researchers learn whether this approach could help future patients.

About this study

In plain language

This study tests how well Soma software finds and measures muscle and fat on CT scans compared with expert doctors. It uses 200 existing scans from people age 16 and older, with personal details removed. There is no contact with patients.

This study evaluates the standalone performance of Soma, a deep-learning software developed by Nucleo Research, Inc. for the automated segmentation of body-composition tissues (skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, and intramuscular adipose tissue) on whole-body computed tomography (CT) images. The aim is to confirm that Soma produces segmentations and tissue-area measurements that agree with a multi-rater expert reference standard, on a diverse cohort representative of demographic and clinical variation. A total of 200 CT scans are sampled by stratified design from a curated pool of 2,066 scans aggregated from six publicly available, de-identified imaging datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, RATIC). Three board-certified radiologists independently annotate the reference standard at the L3 slice. Primary performance is assessed using the Dice similarity coefficient against the multi-rater reference, with predefined thresholds and BCa bootstrap confidence intervals, both in aggregate and within every demographic and clinical subgroup. Secondary endpoints include Bland-Altman analysis of tissue-area agreement, 95th-percentile Hausdorff distance, Pearson correlation of derived indices, and Cohen's kappa for sarcopenia classification using Skeletal Muscle Index (SMI). The study is fully retrospective on de-identified images, involves no patient contact, and has been determined exempt by Salus IRB (Salus Number 26328) under 45 CFR 46.104(d)(4).

Compensation is read from the study's own description and may be missing — always confirm with the study team.

Eligibility

Who can join

  • Age 16 or older when the scan was taken.
  • A CT scan of the belly, with personal details removed, is available from one of six public data sources: autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, or RATIC.
  • The scan includes a clear cross-section at L3, the third bone in the lower back, for measuring muscle and fat.
  • The scan has the details needed to group it, including age, sex, health information recorded by the source, and body mass index (BMI) when available.

Who can't join

  • Under age 16 when the scan was taken.
  • The scan is missing L3, or movement, missing image areas, or metal makes that area too hard to study.
  • The same person already has an identical or nearly identical scan in the study.
  • Details needed to place the scan in a required group are missing.

Simplified for readability. The official criteria below are what the study team uses.

Official eligibility criteria

Inclusion Criteria: * Subjects above 16 years or older at the time the source imaging was acquired. * De-identified abdominal computed tomography (CT) scan available from one of the six predefined publicly available datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, or RATIC). * Scan covers the third lumbar vertebra (L3) with a contiguous axial slice suitable for L3-level body-composition analysis. * Demographic metadata required for stratified sampling (age, sex; BMI where available; clinical context as encoded in source dataset) is present. Exclusion Criteria: * Subject under 16 years of age at the time the source imaging was acquired. * Scan does not include the L3 vertebra or has severe motion artifact, truncation, or metallic artifact precluding analysis at the L3 level. * Duplicate or near-duplicate scans of the same subject already included in the cohort. * Missing demographic metadata required for at least one stratification axis.

Study sites (1)

CA