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

Fujian Medical University (FJMU)

Status: Active, not recruiting

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

Study type: Observational

Funder types: Other

Identifiers

NCT07332923

MRCTA,ECFAH OfFMUI2024]639

Details and patient eligibility

About

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.

Enrollment: 500 estimated patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

• 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

Exclusion criteria

• 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