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

NCT07332923 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2026-01-12

No results posted yet for this study

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

Sponsors & Collaborators

  • First Affiliated Hospital of Fujian Medical University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-08-01
Primary Completion
2026-09-01
Completion
2026-09-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07332923 on ClinicalTrials.gov