NCT07332923

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

Development of a Machine Learning-Based Nomogram for Predicting HIF-2α Expression Levels in Clear Cell Renal Cell Carcinoma

• Status: Active Not Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: First Affiliated Hospital of Fujian Medical University · Academic / Other
• Sex: All
• Age: —
• Healthy volunteers: Not accepted

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

Timeline

• Start date: 2024-08-01
• Primary completion: 2026-09-01
• Completion: 2026-09-01
• First posted: 2026-01-12
• Last updated: 2026-01-12

Locations

1 site across 1 country: China