AI for Renal Tumors Using Non-Contrast CT

NCT07304492 | Not Yet Recruiting DMC

• 1 other identifier: 2509-Exp275
• Study Type: observational
• Target: 10,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Renal Neoplasms; Renal Cyst

Keywords

Artificial intelligence; Renal Neoplasms; Renal Cyst; Computer tomography

Outcome Measures

Primary Outcomes (1)

• Building an intelligent diagnostic system for renal diseases based on CT scans.

  To construct an intelligent system for the detection of renal mass lesions and their differentiation into cysts, benign, and malignant neoplasms.

  1 year

Secondary Outcomes (1)

• Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.

  1 year

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Patients who underwent an abdominal CT examination.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Fudan University: lead

Study Sites (1)

Fudan university Shanghai Cancer Center

Shanghai, Shanghai Municipality, 200032, China

Location

MeSH Terms

Conditions

Kidney Neoplasms

Condition Hierarchy (Ancestors)

Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Central Study Contacts

Yajia Gu, MD

CONTACT

+8621-64175590; guyajia@fudan.edu.cn

Bingni Zhou, MD

CONTACT

+8621-64175590; jobay2621405@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Target Duration: 6 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Head of Radiology, Principal Investigator, Clinical Professor

Study Record Dates

• First Submitted: December 13, 2025
• First Posted: December 26, 2025
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 1, 2028
• Study Completion (Estimated): December 1, 2028
• Last Updated: December 26, 2025

Record last verified: 2025-10

Locations

CN (1)