Preoperative Prediction of Adherent Perirenal Fat. (APF)

Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning: a Prospective, Multicenter Study.

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Radiomics

Detailed Description

Importance:

For patients with kidney tumors requiring surgical treatment, adhesive perirenal fat is a frustrating variable that surgeons encounter during surgery, but the current image-dependent kidney morphometric scoring system used to predict the potential difficulty of surgery ignores this factor. Accurate preoperative prediction of perirenal fat status remains an urgent need.

Purpose:

To determine whether radiomics features of perirenal fat derived from computed tomography images can provide valuable information for judging perirenal fat status, develop a prediction model based on CT radiomics combined with deep learning, and validate the performance of the model in an independent cohort.

Design, setup and participants:

The study included one retrospective dataset and one prospective dataset from four medical centers between January 2020 and September 2023. Kidney plain CT scan was performed in xx adult patients with partial nephrectomy or radical nephrectomy. The training set, validation set, and internal test set were provided by the First Hospital of Jilin University, and the external test set was provided by the First Hospital of Siping City, Liaoyuan Central Hospital and Dongfeng County Hospital. This diagnostic study used single-institution data from January 2020 to May 2023 to extract imaging omics features from the perirenal fat region (independent sample T-test, minimum absolute contraction, and selection operator logistic regression was used to screen for the best imaging omics features). Univariate and multivariate analyses of clinical variables in patients prior to renal surgery were performed to determine independent predictors of adherent perirenal fat in the clinical setting. Different classifiers were used to build prediction models using only the image-omics features and fusion prediction models using independent clinical predictors combined with the image-omics features. Its performance is verified in two test sets.

Main achievements and measures:

The discriminant performance of the image omics model was evaluated by the area under the receiver operating characteristic curve and confirmed by decision curve analysis.

• Study Type: Observational
• Enrollment (Estimated): 500

Contacts and Locations

Study Locations

• China
  • Jilin
    • Ch'ang-ch'un, Jilin, China, 130000
      • Yanbowang

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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.

Description

Inclusion Criteria:

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

Exclusion Criteria:

-(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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Adherent perirenal fat group; The surgeon considers perirenal fat to be adherent.
Non-adherent perirenal fat group; Perirenal fat is considered nonadherent by surgeons.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Radiomics features	Radiomics features related to the prediction of adherent perirenal fat.	From January 2020 to December 2023.

Collaborators and Investigators

• Sponsor: The First Hospital of Jilin University
• Collaborators: Liaoyuan City Central Hospital; SIPING FIRST PEOPLE'S HOSPITAL
• Investigators: Principal Investigator: yanbo wang, The First Hospital of Jilin University

Study record dates

Study Major Dates

• Study Start (Actual): January 5, 2020
• Primary Completion (Estimated): March 1, 2024
• Study Completion (Estimated): December 1, 2024

Study Registration Dates

• First Submitted: August 29, 2023
• First Submitted That Met QC Criteria: September 28, 2023
• First Posted (Actual): October 2, 2023

Study Record Updates

• Last Update Posted (Estimated): January 3, 2024
• Last Update Submitted That Met QC Criteria: January 1, 2024
• Last Verified: June 1, 2023

More Information

Terms related to this study

• Other Study ID Numbers: wangyanbo

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No