Clinical trial · Observational
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (4)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| HIF-2α | — | UNRESOLVED | — |
| Nomogram | — | UNRESOLVED | — |
| Radiomics | — | UNRESOLVED | — |
| Renal Clear Cell Carcinoma | Clear Cell Renal Cell Carcinoma | ALIAS | 0.90 |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- HIF-2α Expression Levels in Clear Cell Renal Cell Carcinoma
- timeFrame
- 1 week
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
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
References
Publications (0)
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