Phenotyping of lumbosacral stenosis in Labrador retrievers using computed tomography.

Vet Radiol Ultrasound 2017 Sep 9;58(5):565-580. Epub 2017 Jul 9.

Cheat Lake Animal Hospital, Morgantown, WV, 26508.

Deep phenotyping tools for characterizing preclinical morphological conditions are important for supporting genetic research studies. Objectives of this retrospective, cross-sectional, methods comparison study were to describe and compare qualitative and quantitative deep phenotypic characteristics of lumbosacral stenosis in Labrador retrievers using computed tomography (CT). Lumbosacral CT scans and medical records were retrieved from data archives at three veterinary hospitals. Using previously published qualitative CT diagnostic criteria, a board-certified veterinary radiologist assigned dogs as either lumbosacral stenosis positive or lumbosacral stenosis negative at six vertebral locations. A second observer independently measured vertebral canal area, vertebral fat area, and vertebral body area; and calculated ratios of vertebral canal area/vertebral body area and vertebral fat area/vertebral body area (fat area ratio) at all six locations. Twenty-five dogs were sampled (lumbosacral stenosis negative, 11 dogs; lumbosacral stenosis positive, 14 dogs). Of the six locations, cranial L6 was the most affected by lumbosacral stenosis (33%). Five of six dogs (83%) with clinical signs of lumbosacral pain were lumbosacral stenosis positive at two or more levels. All four quantitative variables were significantly smaller at the cranial aspects of the L6 and L7 vertebral foramina than at the caudal aspects (P < 0.0001). Fat area ratio was a significant predictor of lumbosacral stenosis positive status at all six locations with cranial L6 having the greatest predictive value (R = 0.43) and range of predictive probability (25-90%). Findings from the current study supported the use of CT as a deep phenotyping tool for future research studies of lumbosacral stenosis in Labrador retrievers.

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http://dx.doi.org/10.1111/vru.12520DOI Listing
September 2017
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