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

NCT07600866 | Not Yet Recruiting

• 1 other identifier: SOMA-26328
• Study Type: observational
• Target: 200
• Locations: 1 country
• Sites: 1

Brief Summary

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).

Conditions

Sarcopenia; Body Composition; Obesity

Keywords

Body Composition; CT; Computed Tomography; Segmentation; Deep Learning; Artificial Intelligence; Skeletal Muscle Index; Sarcopenia; Validation

Outcome Measures

Primary Outcomes (1)

• Dice Similarity Coefficient (DSC) of Soma Segmentation Versus Multi-Rater Radiologist Reference Standard

  Mean Dice Similarity Coefficient (DSC) between Soma-generated segmentation masks and the consensus reference from three board-certified radiologists, computed per tissue class (skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, intramuscular adipose tissue) on all annotated axial slices (every fifth slice across the full scan depth). Predefined performance thresholds: mean DSC greater than or equal to 0.90 for skeletal muscle, subcutaneous adipose, and visceral adipose tissues; mean DSC greater than or equal to 0.85 for intramuscular adipose tissue. Thresholds must be met both in aggregate and within every demographic and clinical subgroup with at least 20 scans (BMI category, age band, sex, body region, clinical context). Reported with 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals.

  Single time point: completion of standalone Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

Secondary Outcomes (4)

• Bland-Altman Agreement for Tissue Cross-Sectional Areas (cm^2)

  Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

• 95th-Percentile Hausdorff Distance Per Tissue Class

  Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

• Pearson Correlation of Tissue Areas and Skeletal Muscle Index (SMI)

  Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

• Cohen's Kappa for Sarcopenia Classification by Skeletal Muscle Index (SMI)

  Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

Study Arms (1)

Public CT Validation Cohort

Two hundred de-identified abdominal CT scans selected by stratified sampling from a curated pool of 2,066 scans aggregated across six publicly available imaging datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, RATIC). Stratification covers BMI category, age band, sex, body region (abdomen-only vs. whole-body), and clinical context (oncologic vs. non-oncologic). Each scan is processed by the Soma software (index test) and independently annotated on every fifth axial slice across the full scan depth by three board-certified radiologists (reference standard).

Diagnostic Test: Soma Body-Composition Segmentation Software

Interventions

Soma Body-Composition Segmentation Software DIAGNOSTIC_TEST

Soma is a deep-learning software pipeline developed by Nucleo Research, Inc. for the automated quantitative analysis of body composition from abdominal CT. It comprises (i) a U-Net segmentation model that delineates skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, and intramuscular adipose tissue on each axial CT slice; and (ii) an EfficientNet-Lite0 + BiLSTM model for automated L3 vertebra detection from axial CT volumes. In this validation study, segmentation performance is assessed on every fifth axial slice across the full scan depth. Outputs include per-tissue segmentation masks, tissue cross-sectional areas (cm\^2), and derived indices including the Skeletal Muscle Index (SMI = muscle area / height\^2). In this study, Soma is applied as the index test in standalone mode, fully blinded to the multi-rater radiologist reference standard.

Public CT Validation Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Subjects older than 16 years old whose de-identified abdominal CT imaging is available from one of six publicly accessible imaging datasets (autoPET, AMOS, MSD Pancreas, CT-ORG, ENHANCE.PET, RATIC). The 200-scan study cohort is selected by stratified sampling from a curated pool of 2,066 scans to ensure representation across BMI category, age band, sex, body region (abdomen-only vs. whole-body), and clinical context (oncologic vs. non-oncologic).

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.

Sponsors & Collaborators

• Nucleo Research, Inc.: lead

Study Sites (1)

Nucleo Research, Inc.

San Francisco, California, 94133, United States

Location

MeSH Terms

Conditions

Sarcopenia; Obesity

Condition Hierarchy (Ancestors)

Muscular Atrophy; Neuromuscular Manifestations; Neurologic Manifestations; Nervous System Diseases; Atrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight

Study Officials

• Luca Pegolotti

  Nucleo Research, Inc.

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Angelica Iacovelli

CONTACT

302-855-3039; info@nucleoresearch.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 15, 2026
• First Posted: May 22, 2026
• Study Start (Estimated): May 31, 2026
• Primary Completion (Estimated): June 15, 2026
• Study Completion (Estimated): June 15, 2026
• Last Updated: May 22, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)