NCT07117786

Computed Tomography Radiomics-Derived Nomogram for Predicting Early Renal Function Decline After Partial Nephrectomy in Renal Cell Carcinoma: A Multicenter Development/Validation Study

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,437 (actual)
• Sponsor: First Affiliated Hospital of Fujian Medical University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

The goal of this observational study is to explore the relationship between CT-based radiomics and postoperative renal function changes in patients with localized renal cell carcinoma (RCC) undergoing partial nephrectomy (PN). The main question it aims to answer is: Can a radiomics-clinical nomogram integrating CT-based radiomics features with preoperative and intraoperative clinical variables accurately predict early postoperative renal function decline in patients with localized RCC undergoing PN? Participants already undergoing renal CT examination and scheduled for postoperative renal function testing as part of the routine perioperative care will receive renal function assessment after completing surgical treatment for RCC.

Conditions

• Renal Cell Carcinoma (RCC)

Interventions

Type	Name	Description
OTHER	CT Contrast	Participants will undergo a CT-based radiomics assessment as part of the intervention. This approach involves the extraction of high-dimensional quantitative imaging features from preoperative contrast-enhanced CT scans, which are then analyzed using machine learning algorithms to identify patterns predictive of early postoperative renal function decline. Unlike conventional radiologic evaluations that rely on visual inspection and basic metrics (e.g., tumor size or enhancement), this radiomics-based intervention captures subtle heterogeneity within renal tumors and surrounding parenchyma. The integration of these features with clinical variables distinguishes this study from other imaging or predictive model studies, enabling the development of a personalized nomogram for patients with localized RCC undergoing partial nephrectomy.
OTHER	Computed Tomography	Participants will undergo a CT-based radiomics assessment as part of the intervention. This approach involves the extraction of high-dimensional quantitative imaging features from preoperative contrast-enhanced CT scans, which are then analyzed using machine learning algorithms to identify patterns predictive of early postoperative renal function decline. Unlike conventional radiologic evaluations that rely on visual inspection and basic metrics (e.g., tumor size or enhancement), this radiomics-based intervention captures subtle heterogeneity within renal tumors and surrounding parenchyma. The integration of these features with clinical variables distinguishes this study from other imaging or predictive model studies, enabling the development of a personalized nomogram for patients with localized RCC undergoing partial nephrectomy.

Timeline

• Start date: 2016-01-01
• Primary completion: 2023-05-06
• Completion: 2023-06-01
• First posted: 2025-08-12
• Last updated: 2025-08-12