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Preoperative Prediction of Adherent Perirenal Fat.

NCT06062173 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2024-01-03

No results posted yet for this study

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

Sponsors & Collaborators

  • The First Hospital of Jilin University

    lead OTHER

Principal Investigators

  • yanbo wang · The First Hospital of Jilin University

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-01-05
Primary Completion
2024-03-31
Completion
2024-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06062173 on ClinicalTrials.gov