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

NCT ID: NCT07117786

Last Updated: 2025-08-12

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 1437 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2016-01-01
• Study Completion Date: 2023-06-01

Brief 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)

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: RETROSPECTIVE

Interventions

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.

Intervention Type: 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.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• (1) postoperative pathological confirmation of RCC; (2) preoperative contrast-enhanced CT of the kidney or abdomen.

Exclusion Criteria

• (1) absence of corticomedullary, nephrographic, or excretory phase CT sequences; (2) poor-quality CT images unsuitable for analysis; (3) incomplete clinicopathologic data; (4) missing renal function data during postoperative follow-up; (5) unavailable renal function assessment within two weeks before surgery.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

First Affiliated Hospital of Fujian Medical University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Other Identifiers

MRCTA, ECFAH of FMU [2025]570

Identifier Type: -

Identifier Source: org_study_id