Renal NeoplasmsNCT07304492
AI for Renal Tumors Using Non-Contrast CT
An observational study of Renal Neoplasms and Renal Cyst, run by Fudan University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Renal Neoplasms, Renal Cyst
- How many people
- 10,000 participants
- Where
- 1 siteChina
- Study dates
- Jan 2026to Dec 2028
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Adult, Older adult
Study population: Patients who underwent an abdominal CT examination.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Fudan university Shanghai Cancer Center
Shanghai, Shanghai Municipality, 200032
Yajia Gu, MD, +8621-64175590, guyajia@fudan.edu.cn
Bingni Zhou, MD, +8621-64175590, jobay2621405@126.com
About this study
Official title: 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.
Read the 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.
- Conditions
- Renal Neoplasms, Renal Cyst
- Keywords
- Artificial intelligence, Renal Neoplasms, Renal Cyst, Computer tomography
- Medical subject headings
- Kidney Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 1 year
Secondary outcomes (1)
Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Dec 2025
- Study startJan 2026 (estimated)
- Primary completionDec 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Renal NeoplasmsStudies of the same condition, open and under way.
- More trials run by Fudan UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 10,000 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Fudan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yajia Gu, MD, Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07304492
- Sponsor's protocol ID
- 2509-Exp275
- Status last verified
- Oct 2025
- First submitted
- 13 Dec 2025
- Last updated
- 26 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 26 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT
- ClinicalTrials.gov ID
- NCT07304492
- Acronym / sponsor ID
- 2509-Exp275
- Conditions
- Renal Neoplasms, Renal Cyst
- Keywords
- Artificial intelligence, Renal Neoplasms, Renal Cyst, Computer tomography
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 10,000 participants (estimated)
- Lead sponsor
- Fudan UniversityUniversity, hospital or other
- Study start
- Jan 2026 (estimated)
- Primary completion
- Dec 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 26 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 6 Months
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients who underwent an abdominal CT examination.
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (4 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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 secondary outcome
Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.
Time frame: 1 year
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 26 Dec 2025.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Renal Neoplasms and Renal Cyst, run by Fudan University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 26 Dec 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Yajia Gu, MD (+8621-64175590, guyajia@fudan.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Building an intelligent diagnostic system for renal diseases based on CT scans. (time frame: 1 year). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 26 Dec 2025.