Diagnosis of canine pulmonary thromboembolism by computed tomography and mathematical modelling using haemostatic and inflammatory variables.

There is no evidence-based diagnostic approach for diagnosis of pulmonary thromboembolism (PTE) in dogs. Many dogs with diseases that predispose to thrombosis are hypercoagulable when assessed with thromboelastography (TEG), but no direct link has been established. The aims of this study were: (1) to investigate if diseased dogs with PTE, diagnosed by computed tomography pulmonary angiography (CTPA), had evidence of hypercoagulability by TEG; (2) to characterise haemostatic and inflammatory changes in dogs with PTE; (3) to construct models for prediction of PTE based on combinations of haemostatic and inflammatory variables; and (4) to evaluate the performance of D-dimer measurement for prediction of PTE. Twenty-five dogs were included in this prospective observational study (PTE: n=6; non-PTE: n=19). Clot strength G values did not differ between the PTE and non-PTE groups in tissue factor (TF) or kaolin-activated TEG analyses. Haemostatic and inflammatory variables did not differ between the two groups. Linear discriminant analysis generated a model for prediction of PTE with a sensitivity and specificity of 100% when TF results were used as TEG data, and a model with sensitivity of 83% and specificity of 100% when kaolin results were used as TEG data. Receiver operating characteristic analysis of D-dimer levels showed that a value of >0.3mg/L yielded a sensitivity of 100% and a specificity of 71.4%. In conclusion, the study supports CTPA as method for diagnosing canine PTE, but shows that TEG alone cannot identify dogs with PTE. Models for prediction of PTE were generated, but require further validation.

Texture analysis of pulmonary parenchymateous changes related to pulmonary thromboembolism in dogs - a novel approach using quantitative methods.

BACKGROUND: Diagnosis of pulmonary thromboembolism (PTE) in dogs relies on computed tomography pulmonary angiography (CTPA), but detailed interpretation of CTPA images is demanding for the radiologist and only large vessels may be evaluated. New approaches for better detection of smaller thrombi include dual energy computed tomography (DECT) as well as computer assisted diagnosis (CAD) techniques. The purpose of this study was to investigate the performance of quantitative texture analysis for detecting dogs with PTE using grey-level co-occurrence matrices (GLCM) and multivariate statistical classification analyses. CT images from healthy (n = 6) and diseased (n = 29) dogs with and without PTE confirmed on CTPA were segmented so that only tissue with CT numbers between -1024 and -250 Houndsfield Units (HU) was preserved. GLCM analysis and subsequent multivariate classification analyses were performed on texture parameters extracted from these images. RESULTS: Leave-one-dog-out cross validation and receiver operator characteristic (ROC) showed that the models generated from the texture analysis were able to predict healthy dogs with optimal levels of performance. Partial Least Square Discriminant Analysis (PLS-DA) obtained a sensitivity of 94% and a specificity of 96%, while Support Vector Machines (SVM) yielded a sensitivity of 99% and a specificity of 100%. The models, however, performed worse in classifying the type of disease in the diseased dog group: In diseased dogs with PTE sensitivities were 30% (PLS-DA) and 38% (SVM), and specificities were 80% (PLS-DA) and 89% (SVM). In diseased dogs without PTE the sensitivities of the models were 59% (PLS-DA) and 79% (SVM) and specificities were 79% (PLS-DA) and 82% (SVM). CONCLUSION: The results indicate that texture analysis of CTPA images using GLCM is an effective tool for distinguishing healthy from abnormal lung. Furthermore the texture of pulmonary parenchyma in dogs with PTE is altered, when compared to the texture of pulmonary parenchyma of healthy dogs. The models' poorer performance in classifying dogs within the diseased group, may be related to the low number of dogs compared to texture variables, a lack of balanced number of dogs within each group or a real lack of difference in the texture features among the diseased dogs.