[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100639654":3},{"organization":4,"armGroups":7,"interventions":14,"overallOfficials":20,"centralContacts":24,"locations":30,"responsibleParty":47,"collaborators":10,"id":49,"slug":50,"hasResults":51,"nctId":52,"briefTitle":53,"officialTitle":54,"acronym":55,"eligibilityCriteria":56,"healthyVolunteers":57,"sex":58,"minAge":59,"maxAge":10,"enrollmentInfo":60,"targetDuration":10,"studyType":63,"phases":10,"briefSummary":64,"conditions":65,"keywords":69,"overallStatus":77,"whyStopped":10,"lastUpdateSubmitDate":78,"lastUpdatePostDateStruct":79,"startDateStruct":82,"completionDateStruct":84,"leadSponsor":86,"locationsCount":87},{"fullName":5,"class":6},"Nucleo Research, Inc.","INDUSTRY",[8],{"label":9,"type":10,"description":11,"interventionNames":12},"Public CT Validation Cohort",null,"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).",[13],"Diagnostic Test: Soma Body-Composition Segmentation Software",[15],{"type":16,"name":17,"description":18,"armGroupLabels":19,"otherNames":10},"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 \u002F height\\^2). In this study, Soma is applied as the index test in standalone mode, fully blinded to the multi-rater radiologist reference standard.",[9],[21],{"name":22,"affiliation":5,"role":23},"Luca Pegolotti","PRINCIPAL_INVESTIGATOR",[25],{"name":26,"role":27,"phone":28,"phoneExt":10,"email":29},"Angelica Iacovelli","CONTACT","302-855-3039","info@nucleoresearch.com",[31],{"facility":5,"status":10,"city":32,"state":33,"zip":34,"country":35,"countryCode":36,"cosmosGeoPoint":37,"geoPoint":42,"contacts":43},"San Francisco","California","94133","United States","US",{"type":38,"coordinates":39},"Point",[40,41],-122.41942,37.77493,{"lat":41,"lon":40},[44],{"name":26,"role":27,"phone":45,"phoneExt":10,"email":46},"+1-415-000-0000","angelica@nucleoresearch.com",{"type":48,"investigatorFullName":10,"investigatorTitle":10,"investigatorAffiliation":10,"oldNameTitle":10,"oldOrganization":10},"SPONSOR","100639654","validation-of-a-body-composition-segmentation-software-on-a-diverse-public-ct-scan-cohort-100639654",false,"NCT07600866","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","SOMA","Inclusion Criteria:\n\n* Subjects above 16 years or older at the time the source imaging was acquired.\n* 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).\n* Scan covers the third lumbar vertebra (L3) with a contiguous axial slice suitable for L3-level body-composition analysis.\n* Demographic metadata required for stratified sampling (age, sex; BMI where available; clinical context as encoded in source dataset) is present.\n\nExclusion Criteria:\n\n* Subject under 16 years of age at the time the source imaging was acquired.\n* Scan does not include the L3 vertebra or has severe motion artifact, truncation, or metallic artifact precluding analysis at the L3 level.\n* Duplicate or near-duplicate scans of the same subject already included in the cohort.\n* Missing demographic metadata required for at least one stratification axis.",true,"ALL","18 Years",{"count":61,"type":62},200,"ESTIMATED","OBSERVATIONAL","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).",[66,67,68],"Sarcopenia","Body Composition","Obesity",[67,70,71,72,73,74,75,66,76],"CT","Computed Tomography","Segmentation","Deep Learning","Artificial Intelligence","Skeletal Muscle Index","Validation","NOT_YET_RECRUITING","2026-05-15",{"date":80,"type":81},"2026-05-22","ACTUAL",{"date":83,"type":62},"2026-05-31",{"date":85,"type":62},"2026-06-15",{"name":5,"class":6},1]