Clinical Research Directory

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

Sponsor: First Affiliated Hospital of Fujian Medical University

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.

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

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