Clinical Research Directory

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.

CD70-targeted immunoPET Imaging of Malignant Cancers

This study aims to establish and optimize the cluster of differentiation (CD70)-targeted immuno-positron emission tomography/computed tomography (immunoPET/CT) imaging method and its physiological and pathological distribution characteristics, based on which the diagnostic efficacy of the above imaging agents in malignant cancers (renal cancer (especially clear cell renal cell carcinoma), lymphoma, or nasopharyngeal carcinoma (NPC)) will be evaluated.

Proteomic Guided First-line Precision Treatment of Renal Clear Cell Carcinoma

Based on different subtypes, subjects will be placed in one of three treatment groups to explore individual efficacy and safety of various treatment regimen.