AI for Renal Tumors Using Non-Contrast CT

NCT ID: NCT07304492

Last Updated: 2025-12-26

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

10000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2026-01-31

Study Completion Date

2028-12-31

Brief Summary

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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

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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.

Conditions

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Renal Neoplasms Renal Cyst

Keywords

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Artificial intelligence Renal Neoplasms Renal Cyst Computer tomography

Study Design

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Observational Model Type

COHORT

Study Time Perspective

OTHER

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Fudan University

OTHER

Sponsor Role lead

Responsible Party

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Yajia Gu, MD

Director, Head of Radiology, Principal Investigator, Clinical Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Fudan university Shanghai Cancer Center

Shanghai, Shanghai Municipality, China

Site Status

Countries

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China

Central Contacts

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Yajia Gu, MD

Role: CONTACT

Phone: +8621-64175590

Email: [email protected]

Bingni Zhou, MD

Role: CONTACT

Phone: +8621-64175590

Email: [email protected]

Facility Contacts

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Yajia Gu, MD

Role: primary

Bingni Zhou, MD

Role: backup

Other Identifiers

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2509-Exp275

Identifier Type: -

Identifier Source: org_study_id