Development of an artificial intelligence-based algorithm for predicting the severity of myxomatous mitral valve disease from thoracic radiographs by using two grading systems.

A heart-convolutional neural network (heart-CNN) was designed and tested for the automatic classification of chest radiographs in dogs affected by myxomatous mitral valve disease (MMVD) at different stages of disease severity. A retrospective and multicenter study was conducted. Lateral radiographs of dogs with concomitant X-ray and echocardiographic examination were selected from the internal databases of two institutions. Dogs were classified as healthy, B1, B2, C and D, based on American College of Veterinary Internal Medicine (ACVIM) guidelines, and as healthy, mild, moderate, severe and late stage, based on Mitral INsufficiency Echocardiographic (MINE) score. Heart-CNN performance was evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP analysis. The area under the curve (AUC) was 0.88, 0.88, 0.79, 0.89 and 0.84 for healthy and ACVIM stage B1, B2, C and D, respectively. According to the MINE score, the AUC was 0.90, 0.86, 0.71, 0.82 and 0.82 for healthy, mild, moderate, severe and late stage, respectively. The developed algorithm showed good accuracy in predicting MMVD stages based on both classification systems, proving a potentially useful tool in the early diagnosis of canine MMVD.

Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs.

An algorithm based on artificial intelligence (AI) was developed and tested to classify different stages of myxomatous mitral valve disease (MMVD) from canine thoracic radiographs. The radiographs were selected from the medical databases of two different institutions, considering dogs over 6 years of age that had undergone chest X-ray and echocardiographic examination. Only radiographs clearly showing the cardiac silhouette were considered. The convolutional neural network (CNN) was trained on both the right and left lateral and/or ventro-dorsal or dorso-ventral views. Each dog was classified according to the American College of Veterinary Internal Medicine (ACVIM) guidelines as stage B1, B2 or C + D. ResNet18 CNN was used as a classification network, and the results were evaluated using confusion matrices, receiver operating characteristic curves, and t-SNE and UMAP projections. The area under the curve (AUC) showed good heart-CNN performance in determining the MMVD stage from the lateral views with an AUC of 0.87, 0.77, and 0.88 for stages B1, B2, and C + D, respectively. The high accuracy of the algorithm in predicting the MMVD stage suggests that it could stand as a useful support tool in the interpretation of canine thoracic radiographs.

Contrast-enhanced ultrasound features of adrenal lesions in dogs.

BACKGROUND: The contrast-enhanced ultrasound (CEUS) features of adrenal lesions are poorly reported in veterinary literature. METHODS: Qualitative and quantitative B-mode ultrasound and CEUS features of 186 benign (adenoma) and malignant (adenocarcinoma and pheochromocytoma) adrenal lesions were evaluated. RESULTS: Adenocarcinomas (n = 72) and pheochromocytomas (n = 32) had mixed echogenicity with B-mode, and a non-homogeneous aspect with a diffused or peripheral enhancement pattern, hypoperfused areas, intralesional microcirculation and non-homogeneous wash-out with CEUS. Adenomas (n = 82) had mixed echogenicity, isoechogenicity or hypoechogenicity with B-mode, and a homogeneous or non-homogeneous aspect with a diffused enhancement pattern, hypoperfused areas, intralesional microcirculation and homogeneous wash-out with CEUS. With CEUS, a non-homogeneous aspect and the presence of hypoperfused areas and intralesional microcirculation can be used to distinguish between malignant (adenocarcinoma and pheochromocytoma) and benign (adenoma) adrenal lesions. LIMITATIONS: Lesions were characterised only by means of cytology. CONCLUSIONS: CEUS examination is a valuable tool for distinction between benign and malignant adrenal lesions and can potentially differentiate pheochromocytomas from adenocarcinomas and adenomas. However, cytology and histology are necessary to obtain the final diagnosis.

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats.

A large overlap in the ultrasound (US) features of focal pancreatic lesions (FPLs) in cats is reported. Furthermore, only a small number of studies describing the contrast-enhanced ultrasound (CEUS) features of FPLs in cats have been conducted today. The aim of this study is to describe the B-mode US and CEUS features of FPLs in cats. Ninety-eight cats cytologically diagnosed with FPL were included. The lesions were classified as adenocarcinoma (n = 40), lymphoma (n = 11), nodular hyperplasia (n = 17), other benign lesion (OBL) (n = 20), cyst (n = 4) or other malignant lesion (OML) (n = 6). Several qualitative and quantitative B-mode and CEUS features were described in each case. OMLs and cysts were not included in the statistical analysis. A decision tree to classify the lesions based on their B-mode and CEUS features was developed. The overall accuracy of the cross-validation of the decision tree was 0.74 (95% CI: 0.63-0.83). The developed decision tree had a very high sensitivity and specificity for nodular hyperplasia (1 and 0.94, respectively) as well as good sensitivity and specificity for both adenocarcinomas (0.85 and 0.77, respectively) and OBLs also (0.70 and 0.93, respectively). The algorithm was unable to detect any specific feature for classifying lymphomas, and almost all the lymphomas were classified as adenocarcinomas. The combination between CEUS and B-mode US is very accurate in the classification of some FPLs, especially nodular hyperplasia and adenocarcinomas. Cytopathology and or histopathology is still a fundamental step FPL diagnostic workflow.

Contrast-enhanced ultrasound features of focal pancreatic lesions in dogs.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) features of pancreatic lesions are poorly reported in veterinary literature. METHODS: Qualitative and quantitative features of pancreatic benign (nodular hyperplasia [NH], cyst and abscess) and malignant (adenocarcinoma and insulinoma) lesions during B-mode and CEUS examinations are described in 75 dogs. RESULTS: Adenocarcinomas (n = 23) had mixed echogenicity at B-mode, and they were hypoenhancing or non-enhancing at CEUS, with a non-homogeneous and cystic enhancement pattern. Insulinomas (n = 23) appeared as hypoechoic lesions at B-mode, and as hyperenhancing, homogeneous and solid lesions at CEUS. NH (n = 17) had an constant appearance, being hypoechoic at ultrasound (US) and isoenhancing at CEUS. Cysts (n = 7) were all anechoic, with acoustic enhancement clearly detectable at US, but were non-enhancing at CEUS. Lastly, abscesses (n = 5) had mixed echogenicity, and they showed both hyperenhancement and non-enhancement at CEUS. Hypoenhancement and non-homogeneous appearance had a moderate diagnostic accuracy in the detection of adenocarcinomas. In particular, hyperenhancement was evident only in malignant lesions (adenocarcinomas and insulinomas). CONCLUSION: CEUS, in combination with B-mode US features, is a valuable tool for distinction of benign and malignant abnormalities of the pancreas and can potentially differentiate insulinomas from adenocarcinomas.

A Machine Learning-Based Approach for Classification of Focal Splenic Lesions Based on Their CT Features.

The aim of the study was to describe the CT features of focal splenic lesions (FSLs) in dogs in order to predict lesion histotype. Dogs that underwent a CT scan and had a FSL diagnosis by cytology or histopathology were retrospectively included in the study. For the statistical analysis the cases were divided into four groups, based on the results of cytopatholoy or hystopathology, namely: nodular hyperplasia (NH), other benign lesions (OBLs), sarcoma (SA), round cell tumour (RCT). Several qualitative and quantitative CT features were described for each case. The relationship occurring between each individual CT feature and the histopathological groups was explred by means of c chi-square test for the count data and by means of Kruskal-Wallis or ANOVA for the continuous data. Furthermore, the main features of each group were described using factorial discriminant analysis, and a decision tree for lesion classification was then developed. Sarcomas were characterised by large dimensions, a cystic appearance and an overall low post contrast-enhancement. NH and OBLs were characterised by small dimensions, a solid appearance and a high post-contrast enhancement. OBLs showed higher post-contrast values than NH. Lastly, RCTs did not exhibit any distinctive CT features. The proposed decision tree had a high accuracy for the classification of SA (0.89) and a moderate accuracy for the classification of OBLs and NH (0.79), whereas it was unable to classify RCTs. The results of the factorial analysis and the proposed decision tree could help the clinician in classifying FSLs based on their CT features. A definitive FSL diagnosis can only be obtained by microscopic examination of the spleen.

Corrigendum: Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.

[This corrects the article DOI: 10.3389/fvets.2021.611556.].

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases.

To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case. Lastly, a machine learning-based decision tree was developed to predict the lesion histotype. Four categories of FLLs - hepatocellular carcinoma (HCC, n = 13), nodular hyperplasia (NH, n = 19), other benign lesions (OBL, n = 18), and other malignant lesions (OML, n = 19) - were evaluated in 69 dogs. Five of the observed qualitative CT features resulted to be statistically significant in the distinction between the 4 categories: surface, appearance, lymph-node appearance, capsule formation, and homogeneity of contrast medium distribution. Three of the observed quantitative CT features were significantly different between the 4 categories: the Hounsfield Units (HU) of the radiologically normal liver parenchyma during the pre-contrast scan, the maximum dimension, and the ellipsoid volume of the lesion. Using the machine learning-based decision tree, it was possible to correctly classify NHs, OBLs, HCCs, and OMLs with an accuracy of 0.74, 0.88, 0.87, and 0.75, respectively. The developed decision tree could be an easy-to-use tool to predict the histotype of different FLLs in dogs. Cytology and histology are necessary to obtain the final diagnosis of the lesions.

Contrast-enhanced ultrasound features of malignant focal liver masses in dogs.

A total of 185 cases (150 retrospectively and 35 prospectively) of malignant liver masses were collected. In the retrospectively collected cases hyperenhancement during wash-in was the most common feature in HCCs but there was a high percentage of cases showing no enhancement or hypo/isoenhancement. ICCs displayed a large variety of contrast enhancement patterns and, although statically significant differences between ICCs and HCCs were evident, no clear distinction between these two pathologies was possible based only on their CEUS appearance. Sarcomas displayed all the possible degrees of wash-in enhancement with non-enhancing being the most common appearance. Metastases displayed all the possible contrast-enhancement patterns, with the most common being hyperenhancement in the wash-in phase followed by hypoenhancement in the wash-out phase. A decision tree was developed based on the features of the retrospectively selected cases. Based on the developed decision tree 27/35 prospectively collected cases were correctly classified. Even if some significant differences among groups were evident, all the histotypes displayed all the possible patterns of contrast enhancement, and, therefore, the differentiation of liver masses in dogs based only on their CEUS features is not feasible and, therefore, cytology or histopathology is required.

Correlation between renal histopathology and renal ultrasound in dogs.

Fifty-three privately owned dogs were included in the study. Ultrasonography of the kidneys was performed ante mortem. All the dogs died or were euthanized for reasons unrelated to this study. Histopathology of both kidneys was performed, and a degeneration and an inflammation score ranging from zero to two was assigned by consensus between two pathologists. A numerical score based on a three level semi-quantitative scale (0, 0.5, 1) was assigned by consensus between two of the authors to the following ultrasonographic abnormalities: cortico-medullary definition, echogenicity of the renal cortex, echogenicity of the medulla, renal shape, cysts, scars, mineralizations, subcapsular perirenal fluid accumulation, pyelectasia. The scores deriving from the consensus were summed to create a summary index called renal ultrasound score (RUS). Statistically significant differences in cortico-medullary definition, echogenicity of the renal cortex, echogenicity of the medulla, renal shape, scars and pyelectasia were evident between the degeneration score groups. There were significantly different distributions of cortico-medullary definition, renal shape and scars between the inflammatory score groups. There were statistically significant differences in the RUS between the degenerative score groups (F = 24.154, p-value<.001). Post-hoc tests revealed significant differences between all groups. There were no significant differences in the RUS between the inflammatory score groups (F = 1.312, p-value = .264). Post-hoc tests revealed no significant differences between groups. The results of the present study suggest that the number and severity of the ultrasonographic abnormalities are correlated with the severity of the kidney degeneration. On the other hand, inflammation showed poor influence on the ultrasonographic appearance of the kidneys.

Contrast-enhanced ultrasound features of hepatocellular carcinoma in dogs.

BACKGROUND: This study aimed to describe the contrast-enhanced ultrasound (CEUS) features of canine hepatocellular carcinoma (HCC) in relation to cellular differentiation and lesion size. METHODS: Sixty dogs with a cytological diagnosis of HCC and that underwent a CEUS examination were retrospectively selected. The wash-in and wash-out patterns of contrast enhancement, along with the time to wash-in and the time to wash-out, of each lesion were recorded. A dimensional cut-off value of 3 cm was adopted for classification. RESULTS: Cellular differentiation had a significant influence on both wash-in (chi-squared=16.99; P<0.001) and wash-out (chi-squared=10.9; P=0.004) patterns of contrast enhancement. Lesion size had a lower, but still significant, influence on both wash-in (chi-squared=12.7; P=0.005) and wash-out (chi-squared=7.42; P=0.024) patterns. A homogeneous hyperenhancement in the arterial phase followed by homogeneous wash-out were suggestive of a well-differentiated HCC. The cellular differentiation of lesions with inhomogeneous hyperenhancement or hypoenhancement/no enhancement as well as an inhomogeneous wash-out or no wash-out could not be inferred. CONCLUSIONS: No significant difference in the time to wash-in and the time to wash-out in relation to cellular differentiation or lesion size was evident. CEUS has the potential to improve efficiency in the diagnosis of HCCs in dogs.

Contrast-enhanced ultrasonography features of hepatobiliary neoplasms in cats.

BACKGROUND: Contrast-enhanced ultrasonography (CEUS) features of primary hepatobiliary neoplasms have been reported in dogs but no information is available in cats. METHODS: Qualitative and quantitative features of bile duct adenomas (BDAs, n=20), bile duct carcinomas (BDCs, n=16), and hepatocellular carcinomas (HCCs, n=8) are described in 44 cats. RESULTS: There was an overlap in CEUS qualitative features between different histotypes, both in wash-in and wash-out phases. Distinction between different neoplasms based only on the CEUS qualitative features was not possible. At peak of enhancement, the BDAs, BDCs and HCCs showed a large range of echogenicities, from hypoenhancement to hyperenhancement, in comparison to the liver parenchyma. Eight of 20 BDAs showed inhomogeneous hyperenhancement during wash-in, which is a feature reported as typical of malignant lesions in dogs. BDC had a significantly faster wash-in compared with both BDA and HCC but the diagnostic accuracy of all the included quantitative variables was only moderate. No significant differences in the wash-out quantitative features of BDA and BDC were evident. CONCLUSION: There is poor evidence that CEUS may be used to distinguish between different primary hepatobiliary neoplasms in cats.

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study.

BACKGROUND: Grading of meningiomas is important in the choice of the most effective treatment for each patient. PURPOSE: To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. STUDY TYPE: Retrospective. POPULATION: In all, 117 meningioma-affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. FIELD STRENGTH/SEQUENCE: 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1 W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2 ). ASSESSMENT: WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception-V3 and AlexNet DCNNs was tested on ADC maps and PCT1 W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. STATISTICAL TEST: Leave-one-out cross-validation. RESULTS: The application of the Inception-V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88-0.98). Remarkably, only 1/38 WHO Grade II-III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59-0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II-III cases. The discriminating accuracy of both DCNNs on postcontrast T1 W images was low, with Inception-V3 displaying an AUC of 0.68 (95% CI, 0.59-0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45-0.64). DATA CONCLUSION: DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1 W images. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152-1159.

A methodological approach for deep learning to distinguish between meningiomas and gliomas on canine MR-images.

BACKGROUND: Distinguishing between meningeal-based and intra-axial lesions by means of magnetic resonance (MR) imaging findings may occasionally be challenging. Meningiomas and gliomas account for most of the total primary brain neoplasms in dogs, and differentiating between these two forms is mandatory in choosing the correct therapy. The aims of the present study are: 1) to determine the accuracy of a deep convolutional neural network (CNN, GoogleNet) in discriminating between meningiomas and gliomas in pre- and post-contrast T1 images and T2 images; 2) to develop an image classifier, based on the combination of CNN and MRI sequence displaying the highest accuracy, to predict whether a lesion is a meningioma or a glioma. RESULTS: Eighty cases with a final diagnosis of meningioma (n = 56) and glioma (n = 24) from two different institutions were included in the study. A pre-trained CNN was retrained on our data through a process called transfer learning. To evaluate CNN accuracy in the different imaging sequences, the dataset was divided into a training, a validation and a test set. The accuracy of the CNN was calculated on the test set. The combination between post-contrast T1 images and CNN was chosen in developing the image classifier (trCNN). Ten images from challenging cases were excluded from the database in order to test trCNN accuracy; the trCNN was trained on the remainder of the dataset of post-contrast T1 images, and correctly classified all the selected images. To compensate for the imbalance between meningiomas and gliomas in the dataset, the Matthews correlation coefficient (MCC) was also calculated. The trCNN showed an accuracy of 94% (MCC = 0.88) on post-contrast T1 images, 91% (MCC = 0.81) on pre-contrast T1-images and 90% (MCC = 0.8) on T2 images. CONCLUSIONS: The developed trCNN could be a reliable tool in distinguishing between different meningiomas and gliomas from MR images.

Endodontic treatment of canine teeth in a cynomolgus monkey ( Macaca fascicularis).

A case of bilateral superior canine tooth pulp necrosis in an 18-year-old male Macaca fascicularis is described . A skull computed tomography scan was performed to confirm the diagnostic suspicion. A successful bilateral endodontic treatment with coronotomy was performed using rotary nickel-titanium instruments. Post-operative dental radiographs confirmed the correct positioning of the sealant. A rapid and excellent clinical recovery was obtained after each surgery. This innovative approach may contribute to improving the quality of root canal treatments provided to non-human primates.

Texture analysis of magnetic resonance images to predict histologic grade of meningiomas in dogs.

OBJECTIVE To predict histologic grade of meningiomas in dogs via texture analysis (TA) of MRI scans of the brain and spinal cord. SAMPLE 58 sets of MRI scans of the brain and spinal cord of dogs with histologically diagnosed meningioma. PROCEDURES MRI sequences were divided into a training set and a test set, and results of histologic assessment were obtained. Tumors were histologically grouped as benign (stage I) or atypical-anaplastic (stage II or III). Texture analysis was performed by use of specialized software on T2-weighted (T2W) and pre- and postcontrast T1-weighted (T1W) images. A set of 30 texture features that provided the highest discriminating power between the 2 histologic classes in the training set was automatically selected by the TA software. Linear discriminant analysis was performed, and the most discriminant factor (MDF) was calculated. The previously selected texture features were then used for linear discriminant analysis of the test set data, and the MDF was calculated. RESULTS For the training set, TA of precontrast T1W images provided the best diagnostic accuracy; a cutoff MDF of < 0.0057 resulted in a sensitivity of 97.4% and specificity of 95.0% for discriminating benign from atypical-anaplastic meningiomas. Use of postcontrast T1W and T2W images yielded poorer diagnostic performances. Application of the MDF cutoff calculated with the training set to the MDF calculated with the test set provided a correct classification rate of 96.8% for precontrast T1W images, 92.0% for postcontrast T1W images, and 78.9% for T2W images. CONCLUSIONS AND CLINICAL RELEVANCE Findings supported the potential clinical usefulness of TA of MRI scans for the grading of meningiomas in dogs.

Relationship of diagnostic accuracy of renal cortical echogenicity with renal histopathology in dogs and cats, a quantitative study.

BACKGROUND: Renal cortical echogenicity is routinely evaluated during ultrasonographic investigation of the kidneys. Both in dog and cat previous ex-vivo studies have revealed a poor correlation between renal echogenicity and corresponding lesions. The aim of this study was to establish the in-vivo relationship between renal cortical echogenicity and renal histopathology. RESULTS: Thirty-eight dogs and fifteen cats euthanized for critical medical conditions were included in the study. Ultrasonographic images of both kidneys were acquired ante mortem at standardized ultrasonographic settings. The echogenicity was quantified by means of Mean Gray Value (MGV) of the renal cortex measured with ImageJ. A complete histopathological examination of both kidneys was performed. Five kidneys were excluded because histopathology revealed neoplastic lesions. Only samples affected by tubular atrophy showed statistically different values in dog, and histopathology explained 13% of the total variance. MGV was not correlated neither to the degeneration nor to the inflammation scores. However, significant differences were identified between mildly and severely degenerated samples. Overall, the classification efficiency of MGV to detect renal lesions was poor with a sensitivity of 39% and a specificity of 86%. In cats, samples affected by both tubular vacuolar degeneration and interstitial nephritis were statistically different and histopathology explained 44% of the total variance. A linear correlation was evident between degeneration and MGV, whereas no correlation with inflammation was found. Statistically significant differences were evident only between normal and severely degenerated samples with a sensitivity of 54.17% and a specificity of 83.3% and MGV resulted scarce to discriminate renal lesions in this species. CONCLUSIONS: Renal cortical echogenicity shows low relevance in detecting chronic renal disease in dog whereas it results worth to identify severe renal damage in cat.

Normal computed tomographic features and reference values for the coelomic cavity in pet parrots.

BACKGROUND: The increasing popularity gained by pet birds over recent decades has highlighted the role of avian medicine and surgery in the global veterinary scenario; such a need for speciality avian medical practice reflects the rising expectation for high-standard diagnostic imaging procedures. The aim of this study is to provide an atlas of matched anatomical cross-sections and contrast-enhanced CT images of the coelomic cavity in three highly diffused psittacine species. RESULTS: Contrast-enhanced computed tomographic studies of the coelomic cavity were performed in 5 blue-and-gold macaws, 4 African grey parrots and 6 monk parakeets by means of a 4-multidetector-row CT scanner. Both pre- and post-contrast scans were acquired. Anatomical reference cross-sections were obtained from 5 blue-and-gold macaw, 7 African grey parrot, and 9 monk parakeet cadavers. The specimens were stored in a -20 °C freezer until completely frozen and then sliced at 5-mm intervals by means of a band saw. All the slices were photographed on both sides. Individual anatomical structures were identified by means of the available literature. Pre- and post-contrast attenuation reference values for the main coelomic organs are reported in Hounsfield units (HU). CONCLUSIONS: The results provide an atlas of matched anatomical cross-sections and contrast-enhanced CT images of the coelomic cavity in three highly diffused psittacine species.

COMPUTED TOMOGRAPHIC FEATURES OF PHARYNGEAL NEOPLASIA IN 25 DOGS.

Computed tomography (CT) is commonly used to investigate head tumors in dogs, however little information is available for lesions of the pharyngeal area. The purpose of this multicentric, retrospective, cross-sectional study was to describe the CT findings in a sample of dogs with pathologically confirmed pharyngeal neoplasia and determine whether any CT features allowed differentiation of tumor type. Location of lesions, size and shape, margins, relationship with surrounding structures and vessels, attenuation characteristics and enhancement pattern, regional lymph node changes, and presence of metastasis were recorded by three observers (1 DECVDI). The effect of final diagnosis on each CT feature was tested. A total of 25 dogs were included: 15 with carcinomas, five sarcomas, four melanomas, and one lymphoma. The oropharynx and laryngopharynx were more frequently involved. Among tumor groups, lesions were of similar size, irregularly shaped, had ill-defined margins, and had moderate-to-marked heterogeneous contrast enhancement. Lysis of hyoid bones was recorded in two carcinomas and infiltration of the lingual artery occurred in one case. Marked medial retropharyngeal lymphoadenomegaly was recorded in 11 of 14 carcinomas, in all sarcomas and in two of four melanomas. The single lymphoma case showed ill-defined thickening of the oropharyngeal and laryngeal wall with retropharyngeal and mandibular lymphadenomegaly. Lung metastases were found in two of five sarcomas and two of four melanomas. Findings from the current study did not support the hypothesis that CT features could be used to predict pharyngeal tumor type in dogs. However, CT was helpful for determining mass extension, lymph node involvement, and distant metastatic spread.