AI for Renal Tumors Using Non-Contrast CT

December 13, 2025 updated by: Yajia Gu, MD, Fudan University

An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT

The goal of this observational study is to learn whether the artificial intelligence method can automatically identify and diagnose renal lesions using non-contrast CT or opportunistic screening.

Study Overview

Status

Not yet recruiting

Conditions

Detailed Description

This study first establishes an AI model capable of effectively detecting and diagnosing kidney lesions based on a multicenter retrospective cohort study. Then, the AI model is applied to a large-scale real-world retrospective and prospective population to validate and improve its effectiveness.

Study Type

Observational

Enrollment (Estimated)

10000

Contacts and Locations

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

Study Contact

Study Contact Backup

Study Locations

  • China
    • Shanghai Municipality
      • Shanghai, Shanghai Municipality, China, 200032
        • Fudan University Shanghai Cancer Center
        • Contact:
        • 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

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Probability Sample

Study Population

Patients who underwent an abdominal CT examination.

Description

Inclusion Criteria:

  1. Patients who underwent an abdominal CT examination.
  2. Patients with renal lesions were managed according to standard clinical pathways, which included follow-up, biopsy, or surgery.
  3. Malignant lesions were pathologically confirmed; benign lesions were confirmed by either pathological diagnosis or imaging follow-up.
  4. No prior treatment had been received for the renal disease.

Exclusion Criteria:

  1. Patients refuse to undergo recommended follow-up, biopsy, or surgery, which precluded definitive diagnosis of the renal lesion.
  2. Absence of complete pathological confirmation for lesions suspected to be malignant.
  3. Patients have received any form of prior treatment for the renal lesion.
  4. Poor image quality that hampered diagnostic evaluation.

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

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Building an intelligent diagnostic system for renal diseases based on CT scans.
Time Frame: 1 year
To construct an intelligent system for the detection of renal mass lesions and their differentiation into cysts, benign, and malignant neoplasms.
1 year

Secondary Outcome Measures

Outcome Measure
Time Frame
Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.
Time Frame: 1 year
1 year

Collaborators and Investigators

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

Sponsor

Fudan University

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 (Estimated)

January 1, 2026

Primary Completion (Estimated)

December 1, 2028

Study Completion (Estimated)

December 1, 2028

Study Registration Dates

First Submitted

December 13, 2025

First Submitted That Met QC Criteria

December 13, 2025

First Posted (Actual)

December 26, 2025

Study Record Updates

Last Update Posted (Actual)

December 26, 2025

Last Update Submitted That Met QC Criteria

December 13, 2025

Last Verified

October 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2509-Exp275

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

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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