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

N/AActive Not RecruitingNCT07332923

First Affiliated Hospital of Fujian Medical University500 enrolled

Overview

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.

Study Type

OBSERVATIONAL

Enrollment

500

Conditions

Renal Clear Cell Carcinoma HIF-2α Radiomics Nomogram

Eligibility

Sex: ALL

Medical Language ↔ Plain English

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

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

Overview

Conditions

Eligibility

Locations (1)

Outcomes

Primary Outcomes