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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07332923

Enrollment

500

Registered

2026-01-12

Start date

2024-08-01

Completion date

2026-09-01

Last updated

2026-01-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

HIF-2α, Nomogram, Radiomics, Renal Clear Cell Carcinoma

Keywords

Nomogram, Radiomics, HIF-2α, Renal clear cell carcinoma

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

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

Design outcomes

Primary

Measure	Time frame
HIF-2α Expression Levels in Clear Cell Renal Cell Carcinoma	1 week

Countries

China

Outcome results