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NCT Number: NCT07304492

AI for Renal Tumors Using 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.

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Key information

Conditions

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

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Observational

Primary location

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • 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.

Exclusion criteria

  • 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.

Treatment and study plan

Primary outcomes

  1. 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.

Secondary outcomes

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

    Time frame: 1 year

Study contacts

Contact information is provided by the study sponsor or research team.

Bingni Zhou, MD

CONTACT

[email protected]

+8621-64175590

Yajia Gu, MD

CONTACT

[email protected]

+8621-64175590

Sponsors and collaborators

Lead sponsor

Fudan University

Other

Registry information

Official study title

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

Important dates

Study start
2026
Primary completion
2028
Study completion
2028
First posted
Dec 26, 2025
Registry last updated
Dec 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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