AI for Renal Tumors Using Non-Contrast CT
An Artificial Intelligence Model for Screening and Diagnosis of Renal Tumors Based on Non-Contrast CT
Study Overview
Status
Status
Conditions
Conditions
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Yajia Gu, MD
- Phone Number: +8621-64175590
- Email: guyajia@fudan.edu.cn
Study Contact Backup
- Name: Bingni Zhou, MD
- Phone Number: +8621-64175590
- Email: jobay2621405@126.com
Study Locations
-
-
Shanghai Municipality
-
Shanghai, Shanghai Municipality, China, 200032
- Fudan University Shanghai Cancer Center
-
Contact:
- Yajia Gu, MD
- Phone Number: +8621-64175590
- Email: guyajia@fudan.edu.cn
-
Contact:
- Bingni Zhou, MD
- Phone Number: +8621-64175590
- Email: jobay2621405@126.com
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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 year
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
Further develop artificial intelligence model to effectively diagnose pathological types of common renal tumors.
Time Frame: 1 year
|
1 year
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- 2509-Exp275
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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