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

Starting soon

Conditions studied: Sarcopenia, Body Composition, Obesity

In brief

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).

Key facts

Study ID

NCT07600866

Run by

Nucleo Research, Inc.

People needed

200

Starts

2026-05-31

Expected to finish

2026-06-15

Last updated by the study team

2026-05-22

Who can join

Age: 18 and older. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• 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.

You may not qualify if…

• 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.

Where it is running

• Nucleo Research, Inc. — San Francisco, California, United States

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.