Objectives: To validate a visual rating scale reflecting sub-regional patterns of putaminal hypointensity in susceptibility-weighted imaging of patients with multiple system atrophy (MSA).Methods: Using a visual rating scale (from G0 to G3), 2 examiners independently rated putaminal hypointensities of 37 MSA patients and 21 control subjects. To investigate the correlation with the scales, R2* values and the volume of the entire putamen were measured.Results: MSA patients with parkinsonian variant had significantly higher scores than those with cerebellar variant. Visual rating scores in MSA were correlated with R2* values [General estimating equation (GEE), Wald chi-square = 25.89, corrected p < 0.001] and volume (Wald chi-square = 75.44, corrected p < 0.001). They correlated with UPDRS motor scores. Binary logistic regression analyses revealed that the visual rating scale was a significant predictor for discriminating MSA patients from controls [multivariate model adjusted for age and sex, odds ratio 52.722 (corrected p = 0.009)]. Pairwise comparison between areas under the curve (AUCs) revealed that the visual rating scale demonstrated higher accuracy than R2* values [difference between AUCs; univariate model = 0.247 (corrected p < 0.001); multivariate model = 0.186 (corrected p = 0.003)]. There were no significant differences in clinical characteristics between the high-iron group, defined as putamen with visual rating scale ≥ G2 and R2* values ≥ third quartile, and the remaining patients.Conclusion: The visual rating scale, which reflects quantitative iron content and atrophy of the putamen as well as motor severities, could be useful for the discrimination and evaluation of patients with MSA.
Acute ischemic stroke is a disease with multiple etiologies. Classifying the mechanism of acute ischemic stroke is therefore fundamental for treatment and secondary prevention. The TOAST classification is currently the most widely-used system, but it has limitations of often classifying unknown causes and inadequate inter-rater reliability. Therefore, we tried to develop a 3D-convolutional neural network(CNN) based algorithm for stroke lesion segmentation and subtype classification using only diffusion and adc information of acute ischemic stroke patients. The recruited patients were 2251 patients with acute ischemic stroke who visited Chungbuk National University Hospital from February 2013 – July 2019. Our segmentation model for lesion segmentation in the training set achieved a Dice score of 0.843±0.009. The subtype classification model achieved an average accuracy of 81.9%, and each subtype was Large artery astherosclerosis (LAA) = 81.6%, Cardioembolism (CE) = 86.8%, Small vessel occlusion (SVO) = 72.9%, and Control = 86.3%. In conclusion, the proposed method shows great potential for identification of diffusion lesion segmentation and stroke subtype classification. As deep learning systems gradually develop, it would be useful in clinical practice and application.
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