Elsevier

Academic Radiology

Volume 22, Issue 11, November 2015, Pages 1376-1384
Academic Radiology

Original Investigation
Reliability of Total Renal Volume Computation in Polycystic Kidney Disease From Magnetic Resonance Imaging

https://doi.org/10.1016/j.acra.2015.06.018Get rights and content

Rationale and Objectives

Total renal volume (TRV) is an important quantitative indicator of the progression of autosomal dominant polycystic kidney disease (ADPKD). The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease proposes a method for TRV computation based on manual tracing and geometric modeling. Alternative approaches for TRV computation are represented by the application of advanced image processing techniques. In this study, we aimed to compare TRV estimates derived from these two different approaches.

Materials and Methods

The nearly automated technique for the analysis of magnetic resonance (MR) images was tested on 30 ADPKD patients. TRV was computed from both axial (KVax) and coronal (KVcor) acquisitions and compared to measurements based on geometric modeling (KVap) by linear regression and Bland–Altman analysis. In addition, to assess reproducibility, intraobserver and interobserver variabilities were computed.

Results

Linear regression analysis between KVax and KVcor resulted in an excellent correlation (KVax = 1KVcor − 0.78; r2 = 0.997). Bland–Altman analysis showed a negligible bias and narrow limits of agreement (bias: −11.7 mL; SD: 54.3 mL). Similar results were obtained by comparison of volumes obtained applying the nearly automated method and the one based on geometric modeling (y = 0.98x + 75.9; r2 = 0.99; bias: −53.7 mL; SD: 108.1 mL). Importantly, geometric modeling does not provide reliable TRV estimates in huge kidney affected by regional deformation. Intraobserver and interobserver variability resulted in very small percentage error <2%.

Conclusions

The results of this study provide the feasibility of using a nearly automated approach for accurate and fast evaluation of TRV also in markedly enlarged ADPKD kidneys including exophytic cysts.

Section snippets

Patients and Imaging Acquisition

A cohort of 30 ADPKD patients (23 patients with normal renal function and seven patients with chronic kidney disease) aged 26–72 years (45 ± 12 years) were enrolled and underwent the MRI study. In all patients, ADPKD had been previously diagnosed with echographic investigation and based on Ravine criteria (16).

MRI data were acquired using a 1.5T scanner (Intera Achieva; Philips Medical System). The imaging protocol included unenhanced sequences only.

T2-weighted turbo spin-echo sequences with

Results

The automated method was successfully applied to all MR images in all patients. Thirteen of them had exophytic cysts for a total count of 43 cysts. In some acquired slices, the cysts were completely detached from the kidney, and the inclusion in the kidney volume of these sections was not possible using the proposed method.

Two examples of the detected contours in two patients from coronal and axial acquisitions are shown in Figure 3a. Examples of the segmentation result in one slice for one

Discussion and conclusions

More than 4 million people are affected by ADPKD worldwide, and currently, very few conclusive treatments are available to slow or prevent the disease that leads to ESRD in most patients. TRV is currently considered an important index to monitor the disease progression, and its assessment is mandatory for targeting therapeutic trials in patients with ADPKD. Up to now, the assessment of TRV has been performed from CT or MR scans by manually tracing renal contours. The proposed technique overcame

Acknowledgments

The authors thank Dott. Enrico Cavagna, Radiology Department of the Infermi Hospital, Rimini, Italy, for data acquisition and the Cassa di Risparmio di Cesena Foundation for supporting D.T. in this research.

References (17)

There are more references available in the full text version of this article.

Cited by (13)

View all citing articles on Scopus
View full text