SarcopeniaSOMANCT07600866
Validation of a Body-Composition Segmentation Software on a Diverse Public CT Scan Cohort
An observational study of Sarcopenia and Body Composition, run by Nucleo Research, Inc. at 1 site in United States.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Sarcopenia, Body Composition, Obesity
- What is being tested?
- Soma Body-Composition Segmentation Software
- Type of treatment
- Injection or infusion
- How many people
- 200 participants
- Where
- 1 siteUnited States
- Study dates
- 31 May 2026to 15 Jun 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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).
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site
Nucleo Research, Inc.
San Francisco, California, 94133
Angelica Iacovelli, +1-415-000-0000, angelica@nucleoresearch.com
About this study
Official title: 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).
Read the 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.
- Conditions
- Sarcopenia, Body Composition, Obesity
- Being tested
- Soma Body-Composition Segmentation Software
- Keywords
- Body Composition, CT, Computed Tomography, Segmentation, Deep Learning, Artificial Intelligence, Skeletal Muscle Index, Sarcopenia, Validation
- Medical subject headings
- Sarcopenia, Obesity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Soma Body-Composition Segmentation SoftwareDiagnostic 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.
| Study group | Role | What they receive |
|---|---|---|
| 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). Receives: Diagnostic Test: Soma Body-Composition Segmentation Software |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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)
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.
Measured: 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
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.
Measured: 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)
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.
Measured: 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)
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.
Measured: Single time point: completion of Soma inference and consolidated multi-rater annotation on all 200 study scans, anticipated within two weeks of study start.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 May 2026
- Study start31 May 2026 (estimated)
- Primary completion15 Jun 2026 (estimated)
- Study completion15 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for SarcopeniaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Nucleo Research, Inc.
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
- Investigators
- Luca Pegolotti, Principal investigator, Nucleo Research, Inc.
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07600866
- Sponsor's protocol ID
- SOMA-26328
- Acronym
- SOMA
- Status last verified
- May 2026
- First submitted
- 15 May 2026
- Last updated
- 22 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 22 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Body-Composition Segmentation Software (Soma) on a Diverse Cohort of Publicly Available CT Scans
- ClinicalTrials.gov ID
- NCT07600866
- Acronym / sponsor ID
- SOMA / SOMA-26328
- Conditions
- Sarcopenia, Body Composition, Obesity
- Keywords
- Body Composition, CT, Computed Tomography, Segmentation, Deep Learning, Artificial Intelligence, Skeletal Muscle Index, Sarcopenia
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Nucleo Research, Inc.Industry (pharma, biotech, devices)
- Study start
- 31 May 2026 (estimated)
- Primary completion
- 15 Jun 2026 (estimated)
- Study completion
- 15 Jun 2026 (estimated)
- First posted
- 22 May 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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).
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Public CT Validation Cohort | – | Diagnostic Test: Soma Body-Composition Segmentation SoftwareTwo 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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).
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Soma Body-Composition Segmentation SoftwareSoma 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. | Diagnostic test | Public CT Validation Cohort |
Primary outcome
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.
4 secondary outcomes
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.
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.
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.
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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Luca Pegolotti principal investigator
Nucleo Research, Inc.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Sarcopenia and Body Composition, run by Nucleo Research, Inc. at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 22 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Angelica Iacovelli (302-855-3039, info@nucleoresearch.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 May 2026.