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

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.

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.