Contrast-enhanced CT-based Deep Learning Model for Preoperative Prediction of Disease-free Survival (DFS) in Localized Clear Cell Renal Cell Carcinoma (ccRCC)

May 27, 2025 updated by: Mingzhao Xiao

Urology Department of the First Affiliated Hospital of Chongqing Medical University

This study aims to preoperatively predict DFS of patients with localised ccRCC using a deep learning prognostic model based on enhanced contrast CT images, validate it's predictive ability in multicentre data and compare it's predictive ability with traditional models.

Study Overview

Status

Recruiting

Conditions

Study Type

Observational

Enrollment (Estimated)

800

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Chongqing
      • Chongqing, Chongqing, China, 400016
        • Recruiting
        • Yingjie Xv
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients admitted to urology departments at participating medical centres

Description

Inclusion Criteria:

  • underwent partial/radical nephrectomies
  • histologically diagnosed as ccRCC
  • with complete clinical data and preoperative CT image data

Exclusion Criteria:

  • with incomplete clinic-pathological data
  • lack of preoperative contrast-enhanced CT images or the image quality was unsuitable for analysis
  • who received pre-surgery neoadjuvant or adjuvant therapies
  • with multiple renal tumors or/and had synchronous metastasis

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Non-recurrence group
Recurrence group

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
disease-free survival (DFS)
Time Frame: recruitment occurred between June 2013 and March 2020
the interval from the date of surgery to disease recurrence, all-cause mortality or the last visit
recruitment occurred between June 2013 and March 2020

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Mingzhao Xiao

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2022

Primary Completion (Estimated)

June 1, 2025

Study Completion (Estimated)

August 1, 2025

Study Registration Dates

First Submitted

October 12, 2023

First Submitted That Met QC Criteria

October 12, 2023

First Posted (Actual)

October 18, 2023

Study Record Updates

Last Update Posted (Actual)

May 31, 2025

Last Update Submitted That Met QC Criteria

May 27, 2025

Last Verified

May 1, 2025

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • DL-ccRCC

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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