[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116895-en":3,"doc-seo-116895-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116895,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis","Conventional MRI findings for canine glioma subtypes and grades show substantial overlap, limiting reliable diagnosis. MRI texture analysis (TA) quantifies the spatial arrangement of pixel intensities to capture tumor heterogeneity in an objective way. This retrospective diagnostic accuracy study evaluates whether machine learning (ML) models trained on MRI-TA can predict intracranial canine gliomas’ histologic types and grades. Dogs with histopathologically confirmed intracranial glioma and available brain MRI were included, with 3D tumors manually segmented across enhancing, non-enhancing, and peri-tumoral vasogenic edema regions and multiple MRI sequences. Using leave-one-out cross-validation, classifiers achieved mean accuracies of 77% for tumor-type discrimination and 75.6% for high-grade prediction, with support vector machine performance up to 94% and 87% respectively.","Received: 23 June 2022 Revised: 16 March 2023 Accepted: 21 March 2023  \nDOI: 10.1111/vru.13242  \nORIGINAL INVESTIGATION  \nMachine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis  \nPablo Barge1   Anna Oevermann2  Arianna Maiolini3  Alexane Durand1  \n1 Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland  \n2 Division of Neurological Sciences, Department of Clinical Research and Veterinary Public Health, Vetsuisse Faculty, University of Bern, Bern, Switzerland  \n3 Division of Clinical Neurology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Bern, Switzerland  \nCorrespondence  \nPablo Barge, Division of Clinical Radiology, Department of Clinical Veterinary Science, Vetsuisse Faculty, University of Bern, Länggassstrasse 128, 3012 Bern, Switzerland. [Email: pablo.barge@unibe.ch](Email: pablo.barge@unibe.ch)  \nAbstract  \nConventional MRI features of canine gliomas subtypes and grades significantly overlap. Texture analysis (TA) quantifies image texture based on spatial arrangement of pixel intensities. Machine learning (ML) models based on MRI-TA demonstrate high accuracy in predicting brain tumor types and grades in human medicine. The aim of this retrospective, diagnostic accuracy study was to investigate the accuracy of ML-based MRI-TAin predicting canine gliomas histologic types and grades. Dogs with histopathological diagnosis of intracranial glioma and available brain MRI were included. Tumors were manually segmented across their entire volume in enhancing part, non-enhancing part, and peri-tumoral vasogenic edema in T2-weighted (T2w), T1-weighted (T1w), FLAIR, and T1w postcontrast sequences. Texture features were extracted and fed into three ML classifiers. Classifiers’ performance was assessed using a leave-one-out cross-validation approach. Multiclass and binary models were built to predict histologic types (oligodendroglioma vs. astrocytoma vs. oligoastrocytoma) and grades (high vs. low), respectively. Thirty-eight dogs with a total of 40 masses were included. Machine learning classifiers had an average accuracy of 77% for discriminating tumor types and of 75.6% for predicting high-grade gliomas. The support vector machine classifier had an accuracy of up to 94% for predicting tumor types and up to 87% for predicting high-grade gliomas. The most discriminative texture features of tumor types and grades appeared related to the peri-tumoral edema inT1w images and to thenon-enhancing part of the tumor in T2w images, respectively. In conclusion, ML-based MRI-TA has the potential to discriminate intracranial canine gliomas types and grades.  \nKEYWORDS  \nartificial intelligence, dog, glial cell neoplasm, radiomics, tumor heterogeneity  \n1  INTRODUCTION  \nConventional MRI features of canine gliomas types and grades largely overlap.1–4 These intra-axial tumors have a predominant localization within the frontal, temporal, and parietal lobes.5 They most  \ncommonly show ovoid to irregular shape, well to poorly defined margins, T2-weighted (T2w) hyperintense, T1-weighted (T1w) isoto hypointense signal, variable degrees of contrast enhancement, intratumoral hemorrhage, peri-tumoral edema, and mass effect. 1,6 Oligodendrogliomas have been reported to more likely contact the  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2023 The Authors. Veterinary Radiology & Ultrasound published by Wiley Periodicals LLC on behalf of American College of Veterinary Radiology.  \n2  \nBARGE ET AL.  \nbrain surface and distort the ventricular system, to have smoother margins and T1w hypointense signal compared to astrocytomas.2–4,7 Neoplastic spread into neighboring brain structures and contrast enhancement have been reported more commonly in hig","cbCaig7SHsLczd5b","https://ap.wps.com/l/cbCaig7SHsLczd5b","pdf",1985230,1,9,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is conventional MRI evaluation challenging for canine glioma typing and grading?\",\"answer\":\"Conventional MRI features for glioma types and grades overlap substantially, making subtle heterogeneous tissue characteristics difficult to distinguish objectively.\"},{\"question\":\"How were MRI data prepared for the texture analysis and machine learning models?\",\"answer\":\"Tumors were manually segmented across the entire enhancing part, non-enhancing part, and peri-tumoral vasogenic edema on T2w, T1w, FLAIR, and T1w postcontrast sequences, from which texture features were extracted.\"},{\"question\":\"What machine learning performance was reported for predicting tumor type and grade?\",\"answer\":\"Across models, average accuracy was 77% for discriminating histologic tumor types and 75.6% for predicting high-grade gliomas; the support vector machine reached up to 94% for types and up to 87% for high-grade prediction.\"}]","Machine learning predicts histologic type and grade of canine gliomas based on MRI texture analysis | PDF",1785672307,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-predicts-histologic-type-and-grade-of-canine-gliomas-based-on-mri-texture-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-predicts-histologic-type-and-grade-of-canine-gliomas-based-on-mri-texture-analysis/116895/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is conventional MRI evaluation challenging for canine glioma typing and grading?","Question",{"text":75,"@type":76},"Conventional MRI features for glioma types and grades overlap substantially, making subtle heterogeneous tissue characteristics difficult to distinguish objectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were MRI data prepared for the texture analysis and machine learning models?",{"text":80,"@type":76},"Tumors were manually segmented across the entire enhancing part, non-enhancing part, and peri-tumoral vasogenic edema on T2w, T1w, FLAIR, and T1w postcontrast sequences, from which texture features were extracted.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning performance was reported for predicting tumor type and grade?",{"text":84,"@type":76},"Across models, average accuracy was 77% for discriminating histologic tumor types and 75.6% for predicting high-grade gliomas; 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