Automatic Segmentation Ultrasound-based Radiomics Technology in Diabetic Kidney Disease

NCT05025540 | Completed

• 1 other identifier: 2021-0465
• Study Type: observational
• Target: 499
• Locations: 1 country
• Sites: 3

Brief Summary

Diabetic kidney disease is a common complication of diabetes and the main cause of end-stage renal disease. In this study, the investigator plan to enroll nearly 500 participant with/without DKD and to develop an automatic segmentation ultrasound based radiomics technology to differentiating participant with a non-invasive and an available way.

Conditions

Diabetic Kidney Disease

Keywords

diabetic kidney disease; ultrasound; radiomics; deep learning; multi-center

Outcome Measures

Primary Outcomes (1)

• AUC

  The area under curve (AUC) of radiomics model for differentiating DKD and T2DM or high level and low level DKD patients

  6 months

Secondary Outcomes (2)

• Miou

  6 months

• mPA

  6 months

Study Arms (2)

Experimental group

Experimental group1：DKD patients with Type 2 diabetes patients with DKD Experimental group2：High level DKD patients with diabetic kidney disease Stage III and IV.

Diagnostic Test: ultrasonic imaging

Control group

Control1：T2DM patients with Type 2 diabetes Control2：Low level DKD patients with diabetic kidney disease Stage I and II.

Diagnostic Test: ultrasonic imaging

Interventions

ultrasonic imaging DIAGNOSTIC_TEST

Two-dimensional ultrasound images of the patient's kidneys were obtained by ultrasound imaging.

Control group; Experimental group

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

1. patients with diabetes 2. patients with or without DKD

You may qualify if:

• patients with clinical diagnosis of T2DM and DKD were enrolled.
• patients with clear B mode ultrasound imaging in both side of kidney (left and right).
• No missing value in the vital clinical data such as eGFR and UACR.

You may not qualify if:

• Patients with large kidney space occupying disease such as kidney renal cyst and tumor were excluded.
• Ultrasound images with severe shadow or incomplete kidney border were excluded.

Sponsors & Collaborators

• Second Affiliated Hospital, School of Medicine, Zhejiang University: lead

Study Sites (3)

The People's Hospital of Yingshang

Fuyang, Anhui, 236200, China

Location

Tianjin Third Central Hospital

Tianjin, Tianjin Municipality, 300000, China

Location

Department of Ultrasound, Second Affiliated Hospital, School of Medicine, Zhejiang University

Hangzhou, Zhejiang, 310000, China

Location

MeSH Terms

Conditions

Diabetic Nephropathies

Interventions

Ultrasonography

Condition Hierarchy (Ancestors)

Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases; Diabetes Complications; Diabetes Mellitus; Endocrine System Diseases

Intervention Hierarchy (Ancestors)

Diagnostic Imaging; Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Pintong Huang

  Department of Ultrasound, The Second Affiliated Hospital of Zhejiang University

  STUDY CHAIR

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 24, 2021
• First Posted: August 27, 2021
• Study Start: June 1, 2021
• Primary Completion: December 1, 2021
• Study Completion: December 1, 2021
• Last Updated: February 16, 2022

Record last verified: 2021-08

Locations

CN (3)