Computed tomography features for differentiating malignant and benign focal liver lesions in dogs: A meta-analysis.

Computed tomography (CT) is often performed to complement ultrasound following detection of focal liver lesions (FLL). There is no consensus in the literature regarding the CT features that might be helpful in the distinction between benign and malignant FLL. The aim of this meta-analysis was to identify, based on the available literature, the qualitative and quantitative CT features able to distinguish between benign and malignant FLL. Studies on the diagnostic accuracy of CT in characterising FLL were searched in MEDLINE, Web of Science, and Scopus databases. Pooled sensitivity, pooled specificity, diagnostic odds ratio (DOR), receiver operator curve (ROC) area, were calculated for qualitative features. DOR were used to determine which qualitative features were most informative to detect malignancy; quantitative features were selected/identified based on standardised mean difference (SMD). Well-defined margins, presence of a capsule, abnormal lymph nodes, and heterogeneity in the arterial, portal and delayed phase were classified as informative qualitative CT features. The pooled sensitivity ranged from 0.630 (abnormal lymph nodes) to 0.786 (well-defined margins), while pooled specificity ranged from 0.643 (well-defined margins) to 0.816 (heterogeneous in delayed phase). Maximum dimensions, ellipsoid volume, attenuation of the liver in the pre-contrast phase, and attenuation of the liver in the arterial, portal, and delayed phase were found to be informative quantitative CT features. Larger maximum dimensions and volume (positive SMD), and lower attenuation values (negative SMD) were more associated with malignancy. This meta-analysis provides the evidence base for the interpreting CT imaging in the characterization of FLL.

Progesterone-responsive vaginal leiomyoma and hyperprogesteronemia due to ovarian luteoma in an older bitch.

BACKGROUND: This is the first report about a vaginal leiomyoma concomitant with an ovarian luteoma in a bitch. CASE PRESENTATION: A 11-year-old intact female Labrador retriever was referred because of anuria, constipation and protrusion of a vaginal mass through the vulvar commissure. The bitch had high serum progesterone concentration (4.94 ng/ml). Because of the possibility of progesterone responsiveness causing further increase of the vaginal mass and since the bitch was a poor surgical candidate a 10 mg/kg aglepristone treatment was started SC on referral day 1. A computerized tomography showed a 12.7 × 6.5 × 8.3 cm mass causing urethral and rectal compression, ureteral dilation and hydronephrosis. A vaginal leiomyoma was diagnosed on histology. As serum progesterone concentration kept increasing despite aglepristone treatment, a 0.02 ng/mL twice daily IM alfaprostol treatment was started on day 18. As neither treatment showed remission of clinical signs or luteolysis, ovariohysterectomy was performed on referral day 35. Multiple corpora lutea were found on both ovaries. On histology a luteoma was diagnosed on the left ovary. P4 levels were undetectable 7 days after surgery. Recovery was uneventful and 12 weeks after surgery tomography showed a reduction of 86.7% of the vaginal mass. The bitch has been in good health and able to urinate without any complication ever since. CONCLUSIONS: This case demonstrates the importance of identifying progesterone related conditions as well as the importance of judiciously using a combined medical and surgical approach.

Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs.

The purpose of this study was to develop a computer-aided detection (CAD) device based on convolutional neural networks (CNNs) to detect cardiomegaly from plain radiographs in dogs. Right lateral chest radiographs (n = 1465) were retrospectively selected from archives. The radiographs were classified as having a normal cardiac silhouette (No-vertebral heart scale [VHS]-Cardiomegaly) or an enlarged cardiac silhouette (VHS-Cardiomegaly) based on the breed-specific VHS. The database was divided into a training set (1153 images) and a test set (315 images). The diagnostic accuracy of four different CNN models in the detection of cardiomegaly was calculated using the test set. All tested models had an area under the curve >0.9, demonstrating high diagnostic accuracy. There was a statistically significant difference between Model C and the remainder models (Model A vs. Model C, P = 0.0298; Model B vs. Model C, P = 0.003; Model C vs. Model D, P = 0.0018), but there were no significant differences between other combinations of models (Model A vs. Model B, P = 0.395; Model A vs. Model D, P = 0.128; Model B vs. Model D, P = 0.373). Convolutional neural networks could therefore assist veterinarians in detecting cardiomegaly in dogs from plain radiographs.

Development of a deep convolutional neural network to predict grading of canine meningiomas from magnetic resonance images.

An established deep neural network (DNN) based on transfer learning and a newly designed DNN were tested to predict the grade of meningiomas from magnetic resonance (MR) images in dogs and to determine the accuracy of classification of using pre- and post-contrast T1-weighted (T1W), and T2-weighted (T2W) MR images. The images were randomly assigned to a training set, a validation set and a test set, comprising 60%, 10% and 30% of images, respectively. The combination of DNN and MR sequence displaying the highest discriminating accuracy was used to develop an image classifier to predict the grading of new cases. The algorithm based on transfer learning using the established DNN did not provide satisfactory results, whereas the newly designed DNN had high classification accuracy. On the basis of classification accuracy, an image classifier built on the newly designed DNN using post-contrast T1W images was developed. This image classifier correctly predicted the grading of 8 out of 10 images not included in the data set.

Use of transfer learning to detect diffuse degenerative hepatic diseases from ultrasound images in dogs: A methodological study.

The aim of this methodological study was to develop a deep convolutional neural network (DNN) to detect degenerative hepatic disease from ultrasound images of the liver in dogs and to compare the diagnostic accuracy of the newly developed DNN with that of serum biochemistry and cytology on the same samples, using histopathology as a standard. Dogs with suspected hepatic disease that had no prior history of neoplastic disease, no hepatic nodular pathology, no ascites and ultrasonography performed 24h prior to death were included in the study (n=52). Ultrasonography and serum biochemistry were performed as part of the routine clinical evaluation. On the basis of histopathology, dogs were categorised as 'normal' (n=8), or having 'vascular abnormalities'(n=8), or 'inflammatory'(n=0), 'neoplastic' (n=4) or 'degenerative'(n=32) disease; dogs with 'neoplastic' disease were excluded from further analysis. On cytological evaluation, dogs were categorised as 'normal' (n=11), or having 'inflammatory' (n=0), 'neoplastic' (n=4) or 'degenerative' (n=37) disease. Dogs were categorised as having 'degenerative' (n=32) or 'non-degenerative' (n=16) liver disease for analysis due to the limited sample size. The DNN was developed using a transfer learning methodology on a pre-trained neural network that was retrained and fine-tuned to our data set. The resultant DNN had a high diagnostic accuracy for degenerative liver disease (area under the curve 0.91; sensitivity 100%; specificity 82.8%). Cytology and serum biochemical markers (alanine transaminase and aspartate transaminase) had poor diagnostic accuracy in the detection of degenerative liver disease. The DNN outperformed all the other non-invasive diagnostic tests in the detection of degenerative liver disease.

Automated computation of femoral angles in dogs from three-dimensional computed tomography reconstructions: Comparison with manual techniques.

The aim of this ex vivo study was to test a novel three-dimensional (3D) automated computer-aided design (CAD) method (aCAD) for the computation of femoral angles in dogs from 3D reconstructions of computed tomography (CT) images. The repeatability and reproducibility of three manual radiography, manual CT reconstructions and the aCAD method for the measurement of three femoral angles were evaluated: (1) anatomical lateral distal femoral angle (aLDFA); (2) femoral neck angle (FNA); and (3) femoral torsion angle (FTA). Femoral angles of 22 femurs obtained from 16 cadavers were measured by three blinded observers. Measurements were repeated three times by each observer for each diagnostic technique. Femoral angle measurements were analysed using a mixed effects linear model for repeated measures to determine the levels of intra-observer agreement (repeatability) and inter-observer agreement (reproducibility). Repeatability and reproducibility of measurements using the aCAD method were excellent (intra-class coefficients, ICCs≥0.98) for all three angles assessed. Manual radiography and CT exhibited excellent agreement for the aLDFA measurement (ICCs≥0.90). However, FNA repeatability and reproducibility were poor (ICCs<0.8), whereas FTA measurement showed slightly higher ICCs values, except for the radiographic reproducibility, which was poor (ICCs<0.8). The computation of the 3D aCAD method provided the highest repeatability and reproducibility among the tested methodologies.

Estimation of fetal lung development using quantitative analysis of ultrasonographic images in normal canine pregnancy.

We investigated the quantitative analysis of sonographic images to predict fetal lung maturity of the canine foetus in normal pregnancy. Twelve bitches were recruited in the present study. Serial ultrasonographic exams were performed at three pre-determined time periods corresponding to the pseudoglandular (40-48 days of pregnancy), canalicular (49-56 days of pregnancy) and saccular phase (57-63 days of pregnancy) of lung development. Mean grey level (MGL) and the standard deviation of the histogram (SDH) of fetal lung and liver sonographic images were measured with dedicated software. The lung-to-liver ratio (LLR) for both parameters was also calculated. Measurements were taken on the two caudal-most foetuses and then averaged. SDH did not show any statistically significant difference between the three time periods in the lungs or in the liver. MGL measured in the lungs significantly increased in the first period and reached a plateau during the last two periods. Liver echogenicity was constant during the first two periods and significantly increased during the last week of gestation. The LLR of MGL significantly decreased during the last week of pregnancy. The LLR was a very good test to detect fetal lung maturity (area under the receiver operator curve (AUROC) = 0.875); using a cut-off value of LLR < 1.541, sensitivity was 83.33% and specificity was 83.33%, positive likelihood ratio = 5. LLR of MGL is an accurate test to estimate lung development in normal canine pregnancies.

Abdominal ultrasound features and reference values in 21 healthy rabbits.

This study aimed to determine the ultrasonographic features and reference values of the abdominal anatomy in mixed-breed dwarf rabbits. Complete abdominal ultrasonographic examination was performed in 21 mixed-breed rabbits (12 males and 9 females) referred for examination to the Department of Animal Medicine, Production and Health, University of Padua, Italy. All animals were sedated during the procedure. The ultrasonographic anatomy of the abdomen was determined, including measurement (mean±SD) of the right kidney (length 2.87±0.34 mm; width 1.62±0.17 mm; height 1.66±0.14 mm) and left kidney (length 2.86±0.33 mm; width 1.72±0.19; height 1.58±0.15 mm), left adrenal gland (width 0.38±0.11 mm; length 0.71±0.14), right adrenal gland (width 0.34±0.08 mm; length 0.73±0.15 mm) and thickness of the walls of the stomach (0.10±0.01 mm), pylorus (0.28±0.04 mm), duodenum (0.19±0.04 mm), sacculus rotundus (0.22±0.06 mm), caecum (0.08±0.01 mm), appendix (0.19±0.04 mm), spiral loop of the ascending colon (0.14±0.04 mm) and distal colon (0.10±0.02 mm). A significant positive correlation between bodyweight and kidney size, adrenal gland length, stomach wall and sacculus rotundus wall was detected.

Evaluation of sedation and clinical effects of midazolam with ketamine or dexmedetomidine in pet rabbits.

The effects of two sedation protocols combining midazolam with ketamine (ketamine group) or dexmedetomidine (dexmedetomidine group) were studied in dwarf companion rabbits undergoing abdominal ultrasound scan. The onset of sedation was faster in the ketamine group; a few rabbits in the dexmedetomidine group required additional doses to lose the righting reflex, although sedation time was not different between groups. A semi-quantitative scale was used to score sedation quality, which was higher in rabbits that received dexmedetomidine rather than ketamine. Pulse rate was lower in the dexmedetomidine group (206 vs 240 bpm), although Doppler blood pressure was higher than in the ketamine group (109 vs 89 mm Hg). Respiratory rate decreased in relation to the baseline values with both protocols but arterial haemoglobin saturation with oxygen was maintained similar to the pre-sedation values throughout the entire procedure, regardless of protocol used and without oxygen supplementation. Both protocols allowed performance of ultrasound scanning, although dexmedetomidine may be preferred if a deep sedation level is required.

A review of diagnostic imaging of snakes and lizards.

Snakes and lizards are considered 'stoic' animals and often show only non-specific signs of illness. Consequently, diagnostic imaging--along with clinical examination and laboratory tests--is gaining importance in making a final diagnosis and establishing a correct therapy. The large number of captive snake and lizard species commonly kept as pets, together with the high inter- and intraspecific morphological variability that is innate in these animals, make the analysis of diagnostic images challenging for the veterinary practitioner. Moreover, a thorough knowledge of the anatomy, physiology and pathology of the species that are the object of clinical investigation is mandatory for the correct interpretation of diagnostic images. Despite the large amount of clinical and scientific work carried out in the past two decades, the radiographic features of snakes and lizards have not undergone systematic description, and therefore veterinarians often have to rely mostly on anatomical studies rather than radiological literature. The aim of this paper is to review the most commonly used diagnostic imaging modalities, as well as to provide an overview of the available international original studies and scientific reviews describing the normal and pathological imaging features in snakes and lizards.

Comparative evaluation of the cadaveric and computed tomographic features of the coelomic cavity in the green iguana (Iguana iguana), black and white tegu (Tupinambis merianae) and bearded dragon (Pogona vitticeps).

Contrast-enhanced computed tomographic studies of the coelomic cavity in four green iguanas, four black and white tegus and four bearded dragons were performed using a conventional CT scanner. Anatomical reference cross sections were obtained from four green iguana, four black and white tegu and six bearded dragon cadavers; the specimens were stored in a -20°C freezer for 24 h then sliced into 5-mm intervals. The frozen sections were cleaned with water and photographed on both sides. The individual anatomical structures were identified by means of the available literature; these were labelled first on the anatomical images and then matched to the corresponding computed tomography images. The results provide an atlas of the normal cross-sectional and computed tomographic anatomy of the coelomic cavity in the green iguana, the black and white tegu and the bearded dragon, which is useful in the interpretation of any imaging modality.