Renal Cell Carcinoma (RCC)
Conditions
Keywords
Renal Function Decline, Renal Cell Carcinoma, Computed Tomography
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.
Interventions
OTHERCT 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.
OTHERComputed 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.
Sponsors
First Affiliated Hospital of Fujian Medical University
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| early postoperative renal function decline | within 3 to 24 months postoperatively | early Renal function decline after PN was defined as a ≥25% reduction in eGFR from the preoperative baseline within 3 to 24 months postoperatively |
Outcome results
None listed