Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning.

Autor: Basran PS; Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.; Department of Biological Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA., Shcherban N; Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA., Forman M; Cornell University Veterinary Specialists, Stamford, Connecticut, USA., Chang J; Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA., Nelissen S; Department of Biomedical Sciences, Section of Anatomic Pathology, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA., Recchia BK; Affectionately Cats Veterinary Hospital, Williston, Vermont, USA., Porter IR; Department of Clinical Sciences, College of Veterinary Medicine, Cornell University, Ithaca, New York, USA.
Jazyk: angličtina
Zdroj: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association [Vet Radiol Ultrasound] 2023 Sep; Vol. 64 (5), pp. 890-903. Date of Electronic Publication: 2023 Jul 02.
DOI: 10.1111/vru.13250
Abstrakt: This retrospective analytical observational cohort study aimed to model and predict the classification of feline intestinal diseases from segmentations of a transverse section from small intestine ultrasound (US) image, complete blood count (CBC), and serum biochemical profile data using a variety of machine-learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy-confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology ("healthy"), and other conditions (warrant a biopsy for further diagnosis). CBC, blood serum chemistry, small intestinal ultrasound, and small intestinal biopsy were obtained within a 2-week interval. CBC and serum biomarkers and radiomic features were combined for modeling. Four classification schemes were investigated: (1) normal versus abnormal; (2) warranting or not warranting a biopsy; (3) lymphoma, IBD, healthy, or other conditions; and (4) lymphoma, IBD, or other conditions. Two feature selection methods were used to identify the top 3, 5, 10, and 20 features, and six machine learning models were trained. The average (95% CI) performance of models for all combinations of features, numbers of features, and types of classifiers was 0.886 (0.871-0.912) for Model 1 (normal vs. abnormal), 0.751 (0.735-0.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450-0.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426-0.589), for Model 4 (lymphoma, IBD, or other). Our findings suggest model accuracies above 0.85 can be achieved in Model 1 and 2, and that including CBC and biochemistry data with US radiomics data did not significantly improve accuracy in our models.
(© 2023 The Authors. Veterinary Radiology & Ultrasound published by Wiley Periodicals LLC on behalf of American College of Veterinary Radiology.)
Databáze: MEDLINE
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