Radiomics of Intra-abdominal and Subcutaneous Adipose Tissue Predict the Efficacy of Bariatric Surgery (RISABS)

July 31, 2025 updated by: Yuntao Nie, China-Japan Friendship Hospital
Using radiomics of intra-abdominal and subcutaneous adipose tissue and clinical features to predict the weight loss efficacy and remission of type 2 diabetes mellitus after bariatric surgery.

Study Overview

Status

Recruiting

Conditions

Detailed Description

In this study, the investigator intend to collect abdominal CT from patients who are proposed to undergo bariatric surgery, to extract the radiomics of intra-abdominal fat and subcutaneous fat, and to establish a prediction model for predicting the efficacy of weight loss and remission of type 2 diabetes mellitus at 1 year, 3 years, and 5 years postoperatively, in conjunction with the clinical data.

Study Type

Observational

Enrollment (Estimated)

800

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 Contact Backup

Study Locations

  • China
      • Beijing, China, 100029
        • Recruiting
        • Yuntao Nie
        • Contact:
        • Contact:
        • Principal Investigator:
          • Yuntao Nie, M.D.

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

Obese patients with type 2 diabetes who will undergo bariatric surgery (sleeve gastrectomy or Roux-en-Y gastric bypass) and receive abdominal CT scan will be included in the study.

Description

Inclusion Criteria:

  • BMI>27.5kg/m2; Type 2 diabetes mellitus; Patients who will undergo bariatric surgery

Exclusion Criteria:

  • Patients without abdominal CT scan; Patients did not undergo sleeve gastrectomy or Roux-en-Y gastric bypass.

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under curve (AUC) of the weight loss prediction model after 1 year
Time Frame: 1 year
This metric shows the discriminatory ability of the radiomic model to predict the probability of inadequate weight loss after 1 year.
1 year
Area under curve (AUC) of the T2DM remission prediction model after 1 year
Time Frame: 1 year
This metric shows the discriminatory ability of the radiomic model to predict the probability of remission of T2DM after 1 year.
1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area under curve (AUC) of the weight loss prediction model after 3 years
Time Frame: 3 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of inadequate weight loss after 3 years.
3 years
Area under curve (AUC) of the T2DM remission prediction model after 3 years
Time Frame: 3 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of remission of T2DM after 3 years.
3 years
Area under curve (AUC) of the weight loss prediction model after 5 years
Time Frame: 5 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of inadequate weight loss after 5 years.
5 years
Area under curve (AUC) of the T2DM remission prediction model after 5 years
Time Frame: 5 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of remission of T2DM after 5 years.
5 years
Area under curve (AUC) of the weight regain model
Time Frame: 5 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of weight regain.
5 years
Area under curve (AUC) of the T2DM relapse model
Time Frame: 5 years
This metric shows the discriminatory ability of the radiomic model to predict the probability of T2DM relapse.
5 years

Collaborators and Investigators

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

Sponsor

China-Japan Friendship Hospital

Investigators

  • Principal Investigator: Yuntao Nie, M.D., China-Japan Friendship Hospital

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 (Actual)

January 1, 2024

Primary Completion (Estimated)

December 31, 2026

Study Completion (Estimated)

December 31, 2027

Study Registration Dates

First Submitted

December 5, 2023

First Submitted That Met QC Criteria

December 5, 2023

First Posted (Actual)

December 13, 2023

Study Record Updates

Last Update Posted (Actual)

August 3, 2025

Last Update Submitted That Met QC Criteria

July 31, 2025

Last Verified

July 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CJBariatric002

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

IPD Plan Description

Subject information involves medical and personal privacy and access is subject to the consent of the principal investigator.

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.

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