{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T04:28:12Z","timestamp":1789705692461,"version":"4.0.1"},"reference-count":26,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,7,2]],"date-time":"2023-07-02T00:00:00Z","timestamp":1688256000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100011179","name":"Cornell Feline Health Center","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100011179","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Vet Radiology Ultrasound"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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\u2010learning approaches. In 149 cats from three institutions, images were obtained from cats with biopsy\u2010confirmed small cell epitheliotropic lymphoma (lymphoma), inflammatory bowel disease (IBD), no pathology (\u201chealthy\u201d), 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\u2010week 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\u20130.912) for Model 1 (normal vs. abnormal), 0.751 (0.735\u20130.818) for Model 2 (biopsy vs. no biopsy), 0.504 (0.450\u20130.556) for Model 3 (lymphoma, IBD, healthy, or other), and 0.531 (0.426\u20130.589), 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.<\/jats:p>","DOI":"10.1111\/vru.13250","type":"journal-article","created":{"date-parts":[[2023,7,3]],"date-time":"2023-07-03T00:14:20Z","timestamp":1688343260000},"page":"890-903","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Combining ultrasound radiomics, complete blood count, and serum biochemical biomarkers for diagnosing intestinal disorders in cats using machine learning"],"prefix":"10.1111","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1573-1549","authenticated-orcid":false,"given":"Parminder S.","family":"Basran","sequence":"first","affiliation":[{"name":"Department of Clinical Sciences College of Veterinary Medicine Cornell University Ithaca New York USA"},{"name":"Department of Biological Sciences College of Veterinary Medicine Cornell University Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalya","family":"Shcherban","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences College of Veterinary Medicine Cornell University Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marnin","family":"Forman","sequence":"additional","affiliation":[{"name":"Cornell University Veterinary Specialists Stamford Connecticut USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmine","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences College of Veterinary Medicine Cornell University Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Nelissen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences Section of Anatomic Pathology College of Veterinary Medicine Cornell University Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin K.","family":"Recchia","sequence":"additional","affiliation":[{"name":"Affectionately Cats Veterinary Hospital Williston Vermont USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ian R.","family":"Porter","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences College of Veterinary Medicine Cornell University Ithaca New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,7,2]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1177\/1098612X19880434"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.5326\/JAAHA-MS-5671"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1939-1676.2009.0457.x"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1177\/1098612X13498596"},{"key":"#cr-split#-e_1_2_13_6_1.1","doi-asserted-by":"crossref","unstructured":"ChangJ PorterIR FormanMA ShcherbanN BasranPS Intra\u2010 and interobserver assessments of intestinal wall thickness and segmentations from transverse sections of feline abdominal ultrasound images. Veterinary Radiology & Ultrasound [Internet]. [cited 2022 Sep 9]","DOI":"10.1111\/vru.13148"},{"key":"#cr-split#-e_1_2_13_6_1.2","unstructured":"Accessed August 1 2022. Available from:https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1111\/vru.13148"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1177\/1098612X14525385"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/1040638716657377"},{"key":"e_1_2_13_9_1","unstructured":"BasranPS PorterI FormanM Machine learning of feline GI disorders using abdominal ultrasound images.OSF;2020; [cited 2021 Aug 27]; Accessed August 1 2022. 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