Predicting HIF-2α Levels in Clear Cell Kidney Cancer Using Machine Learning

NCT07332923 | Active Not Recruiting

• 1 other identifier: MRCTA,ECFAH OfFMUI2024]639
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

This project aims to conduct a multicenter retrospective study to collect clinical, CT imaging, and pathological data from patients. A comprehensive data management system will be established, and radiomic features will be extracted to integrate and analyze multicenter data. We will develop a predictive model based on CT radiomic features and perform both internal and external cohort validation. The model will predict HIF-2α expression levels and clinically relevant prognostic factors in ccRCC, enabling precise identification of patient populations responsive to the HIF-2α antagonist Belzutifan, thereby facilitating personalized treatment decisions, minimizing unnecessary therapeutic risks, and ultimately improving patient quality of life and clinical outcomes.

Conditions

Renal Clear Cell Carcinoma; HIF-2α; Radiomics; Nomogram

Keywords

Nomogram; Radiomics; HIF-2α; Renal clear cell carcinoma

Outcome Measures

Primary Outcomes (1)

• HIF-2α Expression Levels in Clear Cell Renal Cell Carcinoma

  1 week

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

Patients with histologically confirmed renal cell carcinoma

You may qualify if:

• Pathologically confirmed clear cell renal cell carcinoma (ccRCC)
• Availability of comprehensive clinical, pathological, and follow-up information
• Access to preoperative non-contrast and contrast-enhanced CT images through the PACS database
• Adequately preserved pathological slides for subsequent immunohistochemical (IHC) or tissue microarray analysis
• Minimum of one post-treatment follow-up with documented treatment response or efficacy evaluation

You may not qualify if:

• Patients considered ineligible for treatment owing to severe comorbid conditions or inability to undergo any therapeutic intervention
• Patients with concurrent malignancies, including prior treatment for other cancers or presence of untreated active malignancies
• Patients with inadequate CT image quality or missing imaging data
• Patients with missing or incomplete clinical, pathological, or follow-up information

Sponsors & Collaborators

• First Affiliated Hospital of Fujian Medical University: lead

Study Sites (1)

first hospital affiliated of Fujian medical university

Fuzhou, Fujian, 350005, 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 31, 2025
• First Posted: January 12, 2026
• Study Start: August 1, 2024
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: January 12, 2026

Record last verified: 2025-12

Locations

CN (1)