Development of AI Model for Renal Tumor Diagnosis Using CT and Lab Tests

NCT06761742 | Completed

• 1 other identifier: DiagnosisModel_RenalTumor_AI
• Study Type: observational
• Target: 1,922
• Locations: 1 country
• Sites: 1

Brief Summary

This multi-center retrospective study aims to develop a multimodal artificial intelligence diagnostic model using preoperative contrast-enhanced CT images and routine laboratory parameters from patients with renal tumors. The model is designed to assist clinicians in accurately predicting the pathological subtypes of renal tumors preoperatively, enabling detailed diagnoses and advancing precision medicine.

Conditions

Renal Tumors

Outcome Measures

Primary Outcomes (1)

• postoperative pathological report

  From the time of surgery to the release of the postoperative pathological report (typically within 2 weeks post-surgery).

Study Arms (4)

Training Set

Validation Set

Internal Test Set

External Test Set

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study population consists of patients who underwent renal tumor resection at multiple centers, with complete preoperative imaging and laboratory data. Eligible participants include those with pathological diagnoses of clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma. Patients with incomplete imaging data, poor-quality CT images, or coexistence of multiple pathological types of renal tumors were excluded.

You may qualify if:

• Underwent renal tumor resection with a complete postoperative pathological report, and the pathological diagnosis is one of the following types: clear cell renal cell carcinoma, papillary renal cell carcinoma, chromophobe renal cell carcinoma, renal angiomyolipoma, or renal oncocytoma.
• Complete and available four-phase contrast-enhanced CT scans prior to surgery.
• Complete and available routine laboratory test results prior to surgery.

You may not qualify if:

• Incomplete CT data or poor image quality that affects diagnostic analysis.
• A time interval of more than three months between imaging or laboratory testing and pathological diagnosis.
• Patients diagnosed with fat-rich renal angiomyolipoma (AML).
• Pathological diagnosis indicating the coexistence of two or more pathological types of renal tumors.

Sponsors & Collaborators

• RenJi Hospital: lead
• Shanghai Jiao Tong University Affiliated Sixth People's Hospital: collaborator
• Fudan University Pudong Medical Center: collaborator
• Fudan University: collaborator
• The Affiliated Hospital Of Southwest Medical University: collaborator

Study Sites (1)

Shanghai Jiaotong University School of Medicine, Renji Hospital

Shanghai, Shanghai Municipality, 200120, China

Location

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 30, 2024
• First Posted: January 7, 2025
• Study Start: January 1, 2024
• Primary Completion: December 1, 2024
• Study Completion: December 1, 2024
• Last Updated: January 7, 2025

Record last verified: 2024-12

Data Sharing

• IPD Sharing: Will share

Locations

CN (1)