Classification of neoplastic and inflammatory brain disease using MRI texture analysis in 119 dogs. Issue 4 (26th February 2021)

• Record Type: Journal Article
• Authors: Wanamaker, Mason W.; Vernau, Karen M.; Taylor, Sandra L.; Cissell, Derek D.; Abdelhafez, Yasser G.; Zwingenberger, Allison L.
• Is Part Of: Veterinary radiology & ultrasound. Volume 62:Issue 4(2021)
• Page Start: 445
• Page End: 454
• Publication Date: 2021-02-26
• Subjects: MRI -- Meningoencephalitis -- radiomics -- Glioma; Veterinary radiology -- Periodicals; Radiologie vétérinaire -- Périodiques; Échographie vétérinaire -- Périodiques; Radiography -- Periodicals; Ultrasonography -- Periodicals; Diagnosis -- Periodicals; Veterinary Medicine -- Periodicals; Societies, Medical -- Periodicals; 636.089607
• DOI: 10.1111/vru.12962
• Languages: English
• ISSNs: 1058-8183

Abstract

Abstract: Magnetic resonance imaging is the primary method used to diagnose canine glial cell neoplasia and noninfectious inflammatory meningoencephalitis. Subjective differentiation of these diseases can be difficult due to overlapping imaging characteristics. This study utilizes texture analysis (TA) of intra‐axial lesions both as a means to quantitatively differentiate these broad categories of disease and to help identify glial tumor grade/cell type and specific meningoencephalitis subtype in a group of 119 dogs with histologically confirmed diagnoses. Fifty‐nine dogs with gliomas and 60 dogs with noninfectious inflammatory meningoencephalitis were retrospectively recruited and randomly split into training (n = 80) and test (n = 39) cohorts. Forty‐five of 120 texture metrics differed significantly between cohorts after correcting for multiple testing (false discovery rate < 0.05). After training the random forest algorithm, the classification accuracy for the test set was 85% (sensitivity 89%, specificity 81%). TA was only partially able to differentiate the inflammatory subtypes (granulomatous meningoencephalitis [GME], necrotizing meningoencephalitis [NME], and necrotizing leukoencephalitis [NLE]) (out‐of‐bag error rate of 35.0%) and was unable to identify metrics that could correctly classify glioma grade or cell type (out‐of‐bag error rate of 59.6% and 47.5%, respectively). Multiple demographic differences, such as patient age, sex, weight, and breed were identified between disease cohorts and subtypes which may be useful in prioritizing differential diagnoses. TA of MR images with a random forest algorithm provided classification accuracy of inflammatory and neoplastic brain disease approaching the accuracy of previously reported subjective radiologist evaluation.