[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Nucleo Research, Inc.\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":48},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,1,0,[8],{"id":9,"slug":4,"hasResults":10,"nctId":11,"briefTitle":12,"officialTitle":13,"acronym":14,"eligibilityCriteria":15,"healthyVolunteers":16,"sex":17,"minAge":18,"maxAge":4,"enrollmentInfo":19,"targetDuration":4,"studyType":22,"phases":4,"briefSummary":23,"conditions":24,"keywords":28,"overallStatus":36,"whyStopped":4,"lastUpdateSubmitDate":37,"lastUpdatePostDateStruct":38,"startDateStruct":41,"completionDateStruct":43,"leadSponsor":45,"locationsCount":5},"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":20,"type":21},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).",[25,26,27],"Sarcopenia","Body Composition","Obesity",[26,29,30,31,32,33,34,25,35],"CT","Computed Tomography","Segmentation","Deep Learning","Artificial Intelligence","Skeletal Muscle Index","Validation","NOT_YET_RECRUITING","2026-05-15",{"date":39,"type":40},"2026-05-22","ACTUAL",{"date":42,"type":21},"2026-05-31",{"date":44,"type":21},"2026-06-15",{"name":46,"class":47},"Nucleo Research, Inc.","INDUSTRY",""]