Reliability of Total Renal Volume Computation in Polycystic Kidney Disease From Magnetic Resonance Imaging.
OBJECTIVE
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.


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; r(2) = 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; r(2) = 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.
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via Venezia 52, 47521 Cesena, Italy.
Dario
D
Turco
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via Venezia 52, 47521 Cesena, Italy.
Stefano
S
Severi
Nephrology and Dialysis Unit, Infermi Hospital, Rimini, Italy.
Renzo
R
Mignani
Surgical, Medical and Dental Department of Morphological Sciences related to Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy.
Valeria
V
Aiello
Surgical, Medical and Dental Department of Morphological Sciences related to Transplant, Oncology and Regenerative Medicine, University of Modena and Reggio Emilia, Modena, Italy.
Riccardo
R
Magistroni
Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna, Via Venezia 52, 47521 Cesena, Italy. Electronic address: cristiana.corsi3@unibo.it.
Cristiana
C
Corsi
D000328:Adult; D000368:Aged; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007668:Kidney / Q000000981:diagnostic imaging* / Q000473:pathology; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D009929:Organ Size; D016891:Polycystic Kidney, Autosomal Dominant / Q000000981:diagnostic imaging* / Q000473:pathology; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results
Autosomal dominant polycystic kidney disease; kidney segmentation; magnetic resonance imaging; renal volume