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

Burti, Silvia; Zotti, Alessandro; Bonsembiante, Federico; Contiero, Barbara; Banzato, Tommaso

doi:10.3389/fvets.2021.611556

Cited by 8 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decision tree is a machine learning-based tool that has seldom been proposed in the veterinary medical literature for use by the clinician as a guide in interpreting both CEUS ( 19 ) and CT examinations ( 20 , 21 ). In the study by Burti et al ( 19 ), a decision tree was developed on 150 hepatic masses (used as a training set) and tested on another 35 cases (used as a test set).…”

Section: Discussionmentioning

confidence: 99%

“…It should be stated at this point that the classification accuracy for new cases could be lower than the accuracy resulting from cross-validation. The same cross-validation scheme has also been used in other studies ( 20 , 21 ). It is the authors' opinion that, despite the above-mentioned limitations, the proposed decision tree could act as a guide in classifying lesions based on their CEUS features.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats

Burti

Zotti²,

Rubini³

et al. 2022

Front. Vet. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats

Burti

Zotti²,

Rubini³

et al. 2022

Front. Vet. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Decision tree analysis was then performed to detect the best discriminating CT features (a recursive partitioning method was adopted using the rpart package of R – https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf , and a three-step procedure was applied to build the decision tree: (1) the features that provided the best data splitting were selected; (2)10-fold cross-validation was used to prune the decision tree having the lowest number of branches and the lowest misclassification rate ( 19 ); (3) a confusion matrix was built by comparing the values of actual vs predicted samples (obtained from the decision tree classification), and some quality indices regarding model performance were calculated (sensitivity, specificity, accuracy and misclassification rate).…”

Section: Methodsmentioning

confidence: 99%

“…In the last few years, an increasing number of research papers exploring the possible applications of machine learning in veterinary radiology have been published ( 9 – 15 ). Research in this field has mostly been focused on the automatic classification of radiographic images ( 14 , 16 , 17 ), the distinction between benign and malignant brain lesions on MRI ( 10 , 18 ), and the classification of liver focal lesion types on CT images ( 19 ). To the best of the authors' knowledge, the approach of applying machine learning to classify splenic lesions based on their CT appearance has not yet been explored.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…24 They developed an image-based neural network to classify image features in pelvic radiographs. 24 With the advent of CNNs, a number of AI publications have explored the use of CNNs for detecting diseases and abnormalities from medical images, ranging from canine CT and thoracic radiographs, [25][26][27][28] feline thoracic radiographs, 29 canine MRI images, [30][31][32] canine ultrasound images, 33 and dairy cow teat images 34 to optical coherence tomography in dogs. 35 Deep learning has also been recently applied in autosegmentation (the automated contouring of normal tissues in CT scans) in radiotherapy planning of dogs.…”

Section: Companion Animal Care and Image-based Aimentioning

confidence: 99%

The unmet potential of artificial intelligence in veterinary medicine

Basran

Appleby

2022

ajvr

View full text Add to dashboard Cite

Veterinary medicine is a broad and growing discipline that includes topics such as companion animal health, population medicine and zoonotic diseases, and agriculture. In this article, we provide insight on how artificial intelligence works and how it is currently applied in veterinary medicine. We also discuss its potential in veterinary medicine. Given the rapid pace of research and commercial product developments in this area, the next several years will pose challenges to understanding, interpreting, and adopting this powerful and evolving technology. Artificial intelligence has the potential to enable veterinarians to perform tasks more efficiently while providing new insights for the management and treatment of disorders. It is our hope that this will translate to better quality of life for animals and those who care for them.

show abstract

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

Cited by 8 publications

References 31 publications

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats

Contrast-enhanced ultrasound features of focal pancreatic lesions in cats

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

The unmet potential of artificial intelligence in veterinary medicine

Contact Info

Product

Resources

About