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

NCT07117786 | Completed

• 1 other identifier: MRCTA, ECFAH of FMU [2025]570
• Study Type: observational
• Target: 1,437
• Locations: 0 countries
• Sites: N/A

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)

Keywords

Renal Function Decline; Renal Cell Carcinoma; Computed Tomography

Outcome Measures

Primary Outcomes (1)

• early postoperative renal function decline

  early Renal function decline after PN was defined as a ≥25% reduction in eGFR from the preoperative baseline within 3 to 24 months postoperatively

  within 3 to 24 months postoperatively

Interventions

CT Contrast OTHER

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.

Computed Tomography OTHER

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

The study population was drawn from the following institutions: the First Affiliated Hospital of Fujian Medical University, the Second Affiliated Hospital of Fujian Medical University, the First Affiliated Hospital of Xiamen University, the Second People's Hospital affiliated with Fujian University of Traditional Chinese Medicine, the First Affiliated Hospital of Chongqing Medical University, and Quanzhou First Hospital affiliated with Fujian Medical University

You may qualify if:

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

You may not qualify if:

• (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.

Sponsors & Collaborators

• First Affiliated Hospital of Fujian Medical University: lead

MeSH Terms

Conditions

Carcinoma, Renal Cell

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Kidney Neoplasms; Urologic Neoplasms; Urogenital Neoplasms; Neoplasms by Site; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Kidney Diseases; Urologic Diseases; Male Urogenital Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 5, 2025
• First Posted: August 12, 2025
• Study Start: January 1, 2016
• Primary Completion: May 6, 2023
• Study Completion: June 1, 2023
• Last Updated: August 12, 2025

Record last verified: 2025-08

Data Sharing

• IPD Sharing: Will not share