AI for Renal Tumors Using Non-Contrast CT

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

Status

Not yet recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07304492

Enrollment

10000

Registered

2025-12-26

Start date

2026-01-31

Completion date

2028-12-31

Last updated

2025-12-26

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Renal Cyst, Renal Neoplasms

Keywords

Artificial intelligence, Renal Neoplasms, Renal Cyst, Computer tomography

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.

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Age

18 Years to 80 Years

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.

Design outcomes

Primary

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

Secondary

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

Countries

China

Contacts

Primary ContactYajia Gu, MD

guyajia@fudan.edu.cn+8621-64175590

Backup ContactBingni Zhou, MD

jobay2621405@126.com+8621-64175590

Outcome results