Objectives
To estimate renal volume in chronic kidney disease (CKD) patients using a semiautomated software and compare them with split renal function estimates from radionuclide renogram (RR). We proposed that renal volume from unenhanced computed tomography (CT) scans may serve as surrogate marker for assessing renal function in CKD patients.
Materials and Methods
Unenhanced multidetector CT scans of 26 patients with CKD (estimated glomerular filtration rate [eGFR] <60 mL/kg/body surface area [BSA]) and 10 controls (eGFR >60 mL/kg/BSA) were analyzed to calculate renal volumes using a semiautomated software (AMIRAV5.2.0). Volumes obtained were then correlated with corresponding eGFR and split renal function estimates from RR. Volumes were also compared with those obtained on enhanced scans in 10 cases (five disease group, five controls). Bland-Altman analysis was used to assess agreement between methods.
Results
A moderately positive correlation was found between renal volume obtained on unenhanced CT and eGFR ( r = 0.65, P < .0001), whereas a significantly high correlation with split function estimates from RR ( r = 0.95, P < .001) was found. Bland-Altman analysis revealed a good agreement between renal volume from CT and renal function from RR (34/36 observations were within 95% CI and there were two outliers). Correlation between volumes obtained from unenhanced and enhanced CT scans was also significant ( r = 0.96).
Conclusion
In patients with CKD, renal volume derived from unenhanced CT can possibly serve as a surrogate marker for assessing and monitoring renal function reserves to plan further management.
Image-based three-dimensional (3D) volumetry is increasingly being recognized as an important method for assessing organ function reserve, predicting disease burden, and monitoring disease status and treatment response . This is especially gaining significance in renal disorders as functional information is frequently desired along with the morphologic imaging evaluation . Chronic kidney disease (CKD) is a recognized public health problem. It is estimated that more than 2 million Americans will require dialysis or transplantation to manage kidney disease by 2030 . In addition, 19 million adults currently are potentially in the early stages of the disease . Using dynamic contrast-enhanced CT and MR-based approaches, renal function estimation from tracer kinetic modeling is feasible but is usually a complex process that requires technical expertise both in data collection as well as for the 3D volumetric analysis . Moreover, patients with renal insufficiency are often not suitable candidates for intravenous CT or MR contrast media administration . Studies have shown that renal parenchymal volume can predict the functional reserve of the kidneys . Thus renal volume may serve as an important tool for the clinical evaluation of renal diseases and for monitoring progression in an effort to plan for kidney transplantation. In a recent study, the donor renal volume from nonenhanced and contrast-enhanced CT scans correlated with the eGFR, a measure of total renal function . Another study has shown that rapid estimation of split renal function (SRF) in kidney donors can be done using software developed for contrast-enhanced CT renal volumetry in renal donors .
However, the relationship between renal volumes using nonenhanced CT datasets for 3D volumetry for predicting renal function in patients with CKD is largely unexplored. Therefore, we sought to measure renal volume from nonenhanced CT scans in patients with CKD and correlate the total renal volumes and relative percentages (ratio) of each renal volume with the eGFR and corresponding SRF estimates SRF obtained on radionuclide renogram (RR), respectively. We also wanted to assess the relationship between the renal volumes obtained on nonenhanced and contrast-enhanced scans of normal renal donor subjects for whom contrast media administration was feasible and in CKD patients.
Materials and methods
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Subjects
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Imaging Technique
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RR
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Standard of Reference
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Testing Model: CT Volumetry of Renal Parenchyma
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CT-estimated Renal Volumes (CTERV)
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Correlation between CTERV and SRF Estimates
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Results
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CT-estimated Renal Volumes (CTERV)
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Correlation between CTERV and eGFR
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Correlation between CTERV and SRF Estimates
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Table 1
Comparison between Average eGFR and Average Percentages of Renal Parenchymal Function Measured on CT (pCTERV) Obtained on MDCT and Split Renal Function (SRF) on Radionuclide Renogram
Category of Patient Controls ( n = 10) Diseased (CKD) ( n = 26) Mild ( n = 13) Moderate ( n = 5) Severe ( n = 8) Overall ( n = 36) Average Egfr (mL/min/BSA) 85.2 ± 17.8 37.7 ± 16.2 51.1 ± 7 34.2 ± 2.4 18 ± 7.5 50.8 ± 27.1 Average right CTERV (cubic mm) 138.5 ± 51.7 131.2 ± 57.6 129 ± 58.8 141.7 ± 55 122.8 ± 56.4 130.5 ± 55.4 Average right pCTERV (%) 49.4 ± 19.7 44.6 ± 12.1 38.4 ± 14 50.3 ± 3 51 ± 6.7 50 ± 14.5 Right SRF (%) 47.3 ± 22.6 45.5 ± 13.4 38.4 ± 14.1 52.6 ± 7 52.5 ± 8.7 47.3 ± 16.1 Average left CTERV (cubic mm) 153.8 ± 56.2 134.3 ± 18 141.4 ± 56 136.2 ± 41.2 125.2 ± 44 139.2 ± 52.1 Average left pCTERV(%) 50.5 ± 19.7 55.5 ± 12.1 61.7 ± 14 49.6 ± 2.8 49 ± 6.7 50.5 ± 14.5 Left SRF (%) 52.7 ± 22.6 54.5 ± 13.4 61.5 ± 14.1 47.4 ± 7 47.5 ± 8.7 52.7 ± 16.1
BSA, body surface area; CTERV, computed tomography estimated renal volume (the volume derived from three-dimensional CT scans); eGFR, estimated glomerular filtration rate; SRF, split renal function.
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Table 2
Correlation Coefficients and Regression Analysis between CTERV and SRF on Radionuclide Renogram Assessment Renal Function
eGFR (mL/min/BSA) Category Patients ( n ) Pearson Correlation Coefficient ( r ) Student t -test_P_ Value (CI) >60 Controls 10 0.95 1 <.0001 (0.5–0.9) <60 Diseased 26 0.97 0.91 <.0001 (0.8–0.9) 41–60 Mild 13 0.99 0.81 <.0001 (0.9–1) 31–40 Moderate 5 0.90 0.99 .0004 (0.2–0.4) Less than 30 Severe 8 0.91 1 <.0001 (0.5–1) Total Overall 36 0.95 0.95 <.0001 (0.8–0.9)
BSA, body surface area; eGFR, estimated glomerular filtration rate.
The results find a statistically significant correlation between the renal volumes obtained on computed tomography for various categories of chronic kidney disease and the eGFR, higher correlation is found for mild and severe categories of chronic kidney disease.
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Discussion
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Conclusions
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Clinical relevance
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Acknowledgment
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