Preoperative Prediction of Adherent Perirenal Fat.

NCT06062173 | Unknown

• 1 other identifier: wangyanbo
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

In addition to kidney tumor specific factors, adherent perirenal fat is one of the most important causes of technical complications in kidney surgery, and currently, there is a lack of widely used non-invasive predictive models in clinical practice. In this study, a deep learning algorithm based on CT imaging and nomogram was proposed to identify and predict the presence of adherent perirenal fat. This study includes the construction of a prediction model based on CT imaging and the verification of the prediction model.

Conditions

Radiomics

Outcome Measures

Primary Outcomes (1)

• Radiomics features

  Radiomics features related to the prediction of adherent perirenal fat.

  From January 2020 to December 2023.

Study Arms (2)

Adherent perirenal fat group

The surgeon considers perirenal fat to be adherent.

Non-adherent perirenal fat group

Perirenal fat is considered nonadherent by surgeons.

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Preoperative renal CT plain scan imaging data and related clinical data of patients who underwent partial nephrectomy or radical nephrectomy in the First Hospital of Jilin University from January 2022 to December 2022 were retrospectively collected. Imaging data and clinical data from other research centers from June 2023 to September 2023 were prospectively collected. Select the required data according to the exclusion criteria.

You may qualify if:

• （1）Renal tumors, patients requiring surgical treatment. (2) Patients with complete preoperative CT image data.

You may not qualify if:

• (1) Preoperative complications such as acute urinary tract infection, hydronephrosis, pulmonary infection, autoimmune disease, and blood system disease.
• (2) Severe respiratory movement artifacts in CT images. (3) Pregnant or breastfeeding women. (4) Patients who have received immunotherapy or chemoradiotherapy.

Sponsors & Collaborators

• The First Hospital of Jilin University: lead

Study Sites (1)

Yanbowang

Ch’ang-ch’un, Jilin, 130000, China

Location

Study Officials

• yanbo wang

  The First Hospital of Jilin University

  PRINCIPAL INVESTIGATOR

Study Design

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

Study Record Dates

• First Submitted: August 29, 2023
• First Posted: October 2, 2023
• Study Start: January 5, 2020
• Primary Completion: March 1, 2024
• Study Completion: December 1, 2024
• Last Updated: January 3, 2024

Record last verified: 2023-06

Locations

CN (1)