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Opportunistic Detection of Chronic Kidney Disease Using CT-Based Measurements of Kidney Volume and Perirenal Fat
Piotr Białek1, Michał Żuberek2, Adam Dobek1
11st Department of Radiology and Diagnostic Imaging, Medical University of Lodz, Kopcinskiego 22 Street, 90-153 Lodz, Poland.
Insights
Kidney volume and perirenal fat thickness, measured via CT scans, are key indicators for detecting chronic kidney disease (CKD). This imaging approach aids in the early identification of CKD, complementing traditional blood tests.
Area of Science:
- Radiology
- Nephrology
- Medical Imaging
Background:
- Chronic kidney disease (CKD) is a widespread condition often diagnosed late.
- Early detection of CKD is crucial for effective management.
- Adipose tissue distribution, specifically perirenal fat thickness (PrFT), is increasingly recognized for its role in kidney health.
Purpose of the Study:
- To investigate the association between CT-derived fat distribution and kidney morphology parameters with the presence of CKD.
- To develop and validate a predictive model for CKD using imaging biomarkers.
Main Methods:
- A retrospective study of 237 patients who underwent abdominal CT and had serum creatinine data.
- Logistic regression modeling was used, with the dataset split into training (70%) and testing (30%) sets.
- Key parameters measured included kidney volume (KV) and perirenal fat thickness (PrFT), alongside other fat and muscle metrics.
Main Results:
- Kidney volume (KV) and perirenal fat thickness (PrFT) were identified as independent predictors of CKD.
- A simplified model using KV and PrFT showed moderate clinical applicability with an AUC of 0.894 on the test set.
- Higher PrFT and lower KV were significantly associated with CKD.
Conclusions:
- CT-derived kidney volume and perirenal fat thickness are significant independent predictors of CKD.
- A simplified imaging-based model holds potential for opportunistic CKD screening in patients undergoing CT scans.
- These imaging parameters can serve as complementary tools for early CKD detection alongside standard eGFR and creatinine measurements.
Abstract:
Background/Objectives: Chronic kidney disease (CKD) is a prevalent condition with many cases remaining undiagnosed, although early detection is essential. Adipose tissue distribution-particularly perirenal fat thickness (PrFT)-has recently been linked to renal pathophysiology. This study assessed the association between CT-derived parameters of fat distribution and kidney morphology with CKD. Materials and Methods: This retrospective study included 237 patients (117 subjects, 120 controls) who underwent abdominal CT and had serum creatinine data. The dataset was randomly split (70% training, 30% test) to develop and evaluate a logistic regression model. CKD was defined as estimated Glomerular Filtration Rate (eGFR) < 60 mL/min/1.73 m2. PrFT was measured as the distance from the posterior renal capsule to the posterior abdominal wall; renal hilum fat was segmented using a -195 to -45 HU range. Additional parameters (measured using automated segmentation tools) included kidney volume (KV), visceral/subcutaneous fat areas, skeletal muscle area and attenuation, and liver attenuation. Bilateral measurements were averaged. Results: KV (OR = 0.249, 95% CI: 0.146-0.422, p < 0.001) and PrFT (2nd tercile: OR = 7.720, 95% CI: 2.860-20.839; 3rd tercile: OR = 16.892, 95% CI: 5.727-49.822; both p < 0.001) were identified as independent predictors of CKD. These variables were used to construct a simplified model, which demonstrated moderate clinical applicability (AUC = 0.894) when evaluated on the test subset. Conclusions: KV and PrFT emerged as independent predictors of CKD, forming the basis of a simplified model with potential for opportunistic clinical application. This approach may facilitate earlier detection of CKD in patients undergoing CT imaging for unrelated clinical reasons. These imaging parameters are not intended to replace serum creatinine or eGFR but may serve as complementary predictors in specific clinical contexts.
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