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APF: Preoperative Prediction of Adherent Perirenal Fat.

Sponsor
The First Hospital of Jilin University (Other)
Overall Status
Active, not recruiting
CT.gov ID
NCT06062173
Collaborator
SIPING FIRST PEOPLE'S HOSPITAL (Other), Liaoyuan City Central Hospital (Other)
500
Enrollment
1
Location
46.9
Anticipated Duration (Months)
10.7
Patients Per Site Per Month

Study Details

Study Description

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.

Condition or Disease Intervention/Treatment Phase

    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 Design

    Study Type:
    Observational
    Anticipated Enrollment :
    500 participants
    Observational Model:
    Other
    Time Perspective:
    Other
    Official Title:
    Preoperative Prediction of Adherent Perirenal Fat Based on CT Radiomics Combined With Deep Learning: a Prospective, Multicenter Study.
    Actual Study Start Date :
    Jan 5, 2020
    Anticipated Primary Completion Date :
    Oct 1, 2023
    Anticipated Study Completion Date :
    Dec 1, 2023

    Arms and Interventions

    Arm Intervention/Treatment
    Adherent perirenal fat group

    The surgeon considers perirenal fat to be adherent.
    Non-adherent perirenal fat group

    Perirenal fat is considered nonadherent by surgeons.

    Outcome Measures

    Primary Outcome Measures

    1. Radiomics features [From January 2020 to December 2023.]

      Radiomics features related to the prediction of adherent perirenal fat.

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years to 90 Years
    Sexes Eligible for Study:
    All
    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.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Yanbowang Ch'ang-ch'un Jilin China 130000

    Sponsors and Collaborators

    • The First Hospital of Jilin University
    • SIPING FIRST PEOPLE'S HOSPITAL
    • Liaoyuan City Central Hospital

    Investigators

    • Principal Investigator: yanbo wang, The First Hospital of Jilin University

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    The First Hospital of Jilin University
    ClinicalTrials.gov Identifier:
    NCT06062173
    Other Study ID Numbers:
    • wangyanbo
    First Posted:
    Oct 2, 2023
    Last Update Posted:
    Oct 2, 2023
    Last Verified:
    Jun 1, 2023
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No

    Study Results

    No Results Posted as of Oct 2, 2023