Dataset Information

Vestibular Schwannoma Presenting as Acute Vertigo Mimicking Vestibular Neuritis.

ABSTRACT: Vestibular schwannoma (VS) is commonly accompanied by hearing loss, tinnitus, and dizziness and tends to be chronically progressive in nature. We report a case of VS presenting as left vestibular neuritis (VN) in a previously healthy 57-year-old patient. Right-beating horizontal-torsional spontaneous nystagmus was observed, and the bedside head impulse test revealed a left catch-up saccade. The bithermal caloric test showed left canal paresis, and pure-tone audiometry revealed an average threshold of 22.5 dB bilaterally. Brain magnetic resonance imaging (MRI) demonstrated a 0.7-cm enhancing mass in the left internal auditory canal, consistent with VS. The patient was administered with high-dose systemic corticosteroids and vestibular suppressants with antiemetic, which relieved acute vertigo. Although dizziness in VS is chronically progressive in nature, VS may present as an acute vestibular syndrome that mimics VN. VS should be considered a potential cause of acute vestibular syndrome, and thorough neurological examination with MRI may be helpful for accurate diagnosis.

SUBMITTER: Park JY

PROVIDER: S-EPMC9830296 | biostudies-literature | 2022 Sep-Dec

REPOSITORIES: biostudies-literature

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Vestibular Schwannoma Presenting as Acute Vertigo Mimicking Vestibular Neuritis.

Park Joon Yong JY Kim Chang-Hee CH

Case reports in neurology 20220901 3

Vestibular schwannoma (VS) is commonly accompanied by hearing loss, tinnitus, and dizziness and tends to be chronically progressive in nature. We report a case of VS presenting as left vestibular neuritis (VN) in a previously healthy 57-year-old patient. Right-beating horizontal-torsional spontaneous nystagmus was observed, and the bedside head impulse test revealed a left catch-up saccade. The bithermal caloric test showed left canal paresis, and pure-tone audiometry revealed an average thresho ...[more]

PMID: 36636270

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Project description:ObjectiveThe Koos grading scale is a frequently used classification system for vestibular schwannoma (VS) that accounts for extrameatal tumor dimension and compression of the brain stem. We propose an artificial intelligence (AI) pipeline to fully automate the segmentation and Koos classification of VS from MRI to improve clinical workflow and facilitate patient management.MethodsWe propose a method for Koos classification that does not only rely on available images but also on automatically generated segmentations. Artificial neural networks were trained and tested based on manual tumor segmentations and ground truth Koos grades of contrast-enhanced T1-weighted (ceT1) and high-resolution T2-weighted (hrT2) MR images from subjects with a single sporadic VS, acquired on a single scanner and with a standardized protocol. The first stage of the pipeline comprises a convolutional neural network (CNN) which can segment the VS and 7 adjacent structures. For the second stage, we propose two complementary approaches that are combined in an ensemble. The first approach applies a second CNN to the segmentation output to predict the Koos grade, the other approach extracts handcrafted features which are passed to a Random Forest classifier. The pipeline results were compared to those achieved by two neurosurgeons.ResultsEligible patients (n = 308) were pseudo-randomly split into 5 groups to evaluate the model performance with 5-fold cross-validation. The weighted macro-averaged mean absolute error (MA-MAE), weighted macro-averaged F1 score (F1), and accuracy score of the ensemble model were assessed on the testing sets as follows: MA-MAE = 0.11 ± 0.05, F1 = 89.3 ± 3.0%, accuracy = 89.3 ± 2.9%, which was comparable to the average performance of two neurosurgeons: MA-MAE = 0.11 ± 0.08, F1 = 89.1 ± 5.2, accuracy = 88.6 ± 5.8%. Inter-rater reliability was assessed by calculating Fleiss' generalized kappa (k = 0.68) based on all 308 cases, and intra-rater reliabilities of annotator 1 (k = 0.95) and annotator 2 (k = 0.82) were calculated according to the weighted kappa metric with quadratic (Fleiss-Cohen) weights based on 15 randomly selected cases.ConclusionsWe developed the first AI framework to automatically classify VS according to the Koos scale. The excellent results show that the accuracy of the framework is comparable to that of neurosurgeons and may therefore facilitate management of patients with VS. The models, code, and ground truth Koos grades for a subset of publicly available images (n = 188) will be released upon publication.

Dataset Information

Vestibular Schwannoma Presenting as Acute Vertigo Mimicking Vestibular Neuritis.

Publications

Vestibular Schwannoma Presenting as Acute Vertigo Mimicking Vestibular Neuritis.

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