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

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Renal Clear Cell Carcinoma Adenocarcinoma Carcinoma Carcinoma, Renal Cell Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications HIF-2α Kidney Diseases Kidney Neoplasms Male Urogenital Diseases Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Glandular and Epithelial Nomogram Radiomics Urogenital Diseases Urogenital Neoplasms Urologic Diseases Urologic Neoplasms

Sex eligibility

All sexes

Study type

Observational

Primary location

first hospital affiliated of Fujian medical university

Fuzhou, Fujian, 350005, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

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

Time frame: 1 week

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital of Fujian Medical University

Other

Registry information

Official study title

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

Important dates

Study start: 2024
Primary completion: 2026
Study completion: 2026
First posted: Jan 12, 2026
Registry last updated: Jan 12, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

Recruiting