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

May 15, 2026 updated by: Nucleo Research, Inc.

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

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).

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Background. Body composition derived from cross-sectional imaging is increasingly used to assess sarcopenia, cachexia, and metabolic risk across oncology, surgical, and metabolic conditions. Manual segmentation at the L3 vertebra is the established reference but is time-consuming and rater-dependent. Soma is a deep-learning software pipeline (U-Net segmentation of skeletal muscle, subcutaneous adipose tissue, visceral adipose tissue, and intramuscular adipose tissue; EfficientNet-Lite0 + BiLSTM for automated L3 slice detection) developed by Nucleo Research, Inc. to provide reproducible, automated body-composition measurements from routine abdominal CT.

Objective. To validate the standalone segmentation performance of Soma against an expert multi-rater reference standard, in aggregate and across predefined demographic and clinical subgroups.

Design. 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). Stratification ensures representation across: BMI category (4 levels), age band (4 levels), sex (2 levels), body region (abdomen-only vs. whole-body), and clinical context (oncologic vs. non-oncologic).

Reference Standard. Three board-certified radiologists independently annotate the four tissue classes on every fifth axial slice (stride of 5) across the full scan depth. Inter-rater agreement is summarized prior to consolidation; consensus reference is derived per pre-specified consolidation rules.

Index Test. Soma processes each scan blinded to ground truth. Outputs include per-tissue segmentation masks, tissue cross-sectional areas, and downstream indices including Skeletal Muscle Index (SMI = muscle area / height^2).

Primary Endpoint. Mean Dice similarity coefficient between Soma and the multi-rater reference computed across all annotated slices, with predefined performance thresholds: greater than or equal to 0.90 for muscle, subcutaneous adipose, and visceral adipose tissues; greater than or equal to 0.85 for intramuscular adipose tissue. Thresholds must be met both in aggregate AND within every demographic or clinical subgroup with at least 20 scans. 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals are reported.

Secondary Endpoints. (1) Bland-Altman analysis of agreement on tissue cross-sectional areas (bias and 95% limits of agreement). (2) 95th-percentile Hausdorff distance per tissue class. (3) Pearson correlation coefficient for tissue areas and SMI. (4) Cohen's kappa for sarcopenia classification (binary, by sex-specific SMI cutoffs) with prespecified threshold of greater than or equal to 0.80.

Safety, Privacy, and Ethics. The study is fully retrospective on previously collected, publicly available, de-identified imaging data, with no patient contact and no intervention. There is no foreseeable risk to subjects. The protocol has been determined exempt by Salus IRB (Salus Number 26328) under 45 CFR 46.104(d)(4) on 04 May 2026.

Study Type

Observational

Enrollment (Estimated)

200

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

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).

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Dice Similarity Coefficient (DSC) of Soma Segmentation Versus Multi-Rater Radiologist Reference Standard
Time Frame: 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.
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 Outcome Measures

Outcome Measure
Measure Description
Time Frame
Bland-Altman Agreement for Tissue Cross-Sectional Areas (cm^2)
Time Frame: Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.
Bland-Altman analysis comparing tissue cross-sectional areas (in cm^2) computed by Soma versus the multi-rater radiologist reference, for each tissue class (skeletal muscle, subcutaneous adipose, visceral adipose, intramuscular adipose). Reported: mean bias and 95% limits of agreement, in aggregate and within demographic and clinical subgroups.
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
Time Frame: 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 (HD95, in mm) between Soma segmentation contours and the multi-rater radiologist reference, computed per tissue class at the L3 vertebra. Reported in aggregate and within demographic and clinical subgroups.
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)
Time Frame: 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 coefficient (r) between Soma-derived and multi-rater-reference values for: (i) per-tissue cross-sectional areas (skeletal muscle, subcutaneous adipose, visceral adipose, intramuscular adipose, in cm^2); and (ii) Skeletal Muscle Index (SMI = skeletal muscle area / height^2, in cm^2/m^2). Reported with 95% confidence intervals, in aggregate and within subgroups.
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)
Time Frame: 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 coefficient quantifying agreement between Soma-derived and reference-derived binary sarcopenia classification, defined by sex-specific Skeletal Muscle Index (SMI) cutoffs. Prespecified performance threshold: kappa greater than or equal to 0.80. Reported in aggregate and within demographic and clinical subgroups.
Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Nucleo Research, Inc.

Investigators

  • Principal Investigator: Luca Pegolotti, Nucleo Research, Inc.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

May 31, 2026

Primary Completion (Estimated)

June 15, 2026

Study Completion (Estimated)

June 15, 2026

Study Registration Dates

First Submitted

May 15, 2026

First Submitted That Met QC Criteria

May 15, 2026

First Posted (Actual)

May 22, 2026

Study Record Updates

Last Update Posted (Actual)

May 22, 2026

Last Update Submitted That Met QC Criteria

May 15, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SOMA-26328

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Obesity

Clinical Trials on Soma Body-Composition Segmentation Software

Search Similar Trials

Sponsors and Collaborators

Medical Conditions

Drug Interventions

CROs by country

CROs in Portugal

Conditions

Rare Diseases

Drug Interventions

Dietary Supplements

Sponsor/Collaborators

Locations

Subscribe