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Objectives: This study aimed to identify predictors for the recurrence of spontaneous intracranial hypotension (SIH) after epidural blood patch (EBP). Background: Epidural blood patch is the main treatment option for SIH; however, the characteristics of patients who experience relapse after successful EBP treatment for SIH remain understudied. Methods: In this exploratory, retrospective, case–control study, we included 19 patients with SIH recurrence after EBP and 36 age‐ and sex‐matched patients without recurrence from a single tertiary medical institution. We analyzed clinical characteristics, neuroimaging findings, and volume changes in intracranial structures after EBP treatment. Machine learning methods were utilized to predict the recurrence of SIH after EBP treatment. Results: There were no significant differences in clinical features between the recurrence and no‐recurrence groups. Among brain magnetic resonance imaging signs, diffuse pachymeningeal enhancement and cerebral venous dilatation were more prominent in the recurrence group than no‐recurrence group after EBP (14/19 [73%] vs. eight of 36 [22%] patients, p = 0.001; 11/19 [57%] vs. seven of 36 [19%] patients, p = 0.010, respectively). The midbrain–pons angle decreased in the recurrence group compared to the no‐recurrence group after EBP, at a mean (standard deviation [SD]) of –12.0 [16.7] vs. +1.8[18.3]° (p = 0.048). In volumetric analysis, volume changes after EBP were smaller in the recurrence group than in the no‐recurrence group in intracranial cerebrospinal fluid (mean [SD] −11.6 [15.3] vs. +4.8 [17.1] mL, p = 0.001) and ventricles (mean [SD] +1.0 [2.0] vs. +2.0 [2.5] mL, p = 0.003). Notably, the random forest classifier indicated that the model constructed with brain volumetry was more accurate in discriminating SIH recurrence (area under the curve = 0.80 vs. 0.52). Conclusion: Our study suggests that volumetric analysis of intracranial structures may aid in predicting recurrence after EBP treatment in patients with SIH. Plain Language Summary: There is not much research about patients who experience symptom relapse after successful epidural blood patch (EBP) treatment for spontaneous intracranial hypotension (SIH). We used machine learning (an analysis that helps us understand patterns in data) to learn about medical records from 19 patients with SIH whose symptoms came back and 36 patients whose symptoms did not recur after EBP treatment. Our results suggest that changes in the amount of cerebrospinal fluid in the brain and the size of the ventricles can help predict the recurrence of SIH after EBP treatment. [ABSTRACT FROM AUTHOR]

The aim of the present study was to predict amyloid-beta positivity using a conventional T1-weighted image, radiomics, and a diffusion-tensor image obtained by magnetic resonance imaging (MRI). We included 186 patients with mild cognitive impairment (MCI) who underwent Florbetaben positron emission tomography (PET), MRI (three-dimensional T1-weighted and diffusion-tensor images), and neuropsychological tests at the Asan Medical Center. We developed a stepwise machine learning algorithm using demographics, T1 MRI features (volume, cortical thickness and radiomics), and diffusion-tensor image to distinguish amyloid-beta positivity on Florbetaben PET. We compared the performance of each algorithm based on the MRI features used. The study population included 72 patients with MCI in the amyloid-beta-negative group and 114 patients with MCI in the amyloid-beta-positive group. The machine learning algorithm using T1 volume performed better than that using only clinical information (mean area under the curve [AUC]: 0.73 vs. 0.69, p < 0.001). The machine learning algorithm using T1 volume showed better performance than that using cortical thickness (mean AUC: 0.73 vs. 0.68, p < 0.001) or texture (mean AUC: 0.73 vs. 0.71, p = 0.002). The performance of the machine learning algorithm using fractional anisotropy in addition to T1 volume was not better than that using T1 volume alone (mean AUC: 0.73 vs. 0.73, p = 0.60). Among MRI features, T1 volume was the best predictor of amyloid PET positivity. Radiomics or diffusion-tensor images did not provide additional benefits. [ABSTRACT FROM AUTHOR]

Background and purpose: As part of network‐specific neurodegeneration, changes in cerebellar gray matter (GM) volume and impaired cerebello–cerebral functional networks have been reported in Alzheimer disease (AD). Compared with healthy controls, a volume loss in the cerebellum has been observed in patients with continuum of AD. However, little is known about the anatomical or functional changes in patients with clinical AD but no brain amyloidosis. We aimed to identify the relationship between cerebellar volume and dementia conversion of amyloid‐negative mild cognitive impairment (MCI). Methods: This study was a retrospective cohort study of patients over the age 50 years with amyloid‐negative amnestic MCI who visited the memory clinic of Asan Medical Center with no less than a 36‐month follow‐up period. All subjects underwent detailed neuropsychological tests, 3 T brain magnetic resonance imaging scans including three‐dimensional T1 imaging, and fluorine‐18[F18]‐florbetaben amyloid positron emission tomography scans. A spatially unbiased atlas template of the cerebellum and brainstem was used for analyzing cerebellar GM volume. Results: During the 36 months of follow‐up, 39 of 107 (36.4%) patients converted to dementia from amnestic MCI. The converter group had more severe impairments in all visual memory tasks. In terms of volumetric analysis, reduced crus I/II volume adjusted with total intracranial volume, and age was observed in the converter group. Conclusions: Significant cerebellar GM atrophy involving the bilateral crus I/II may be a novel imaging biomarker for predicting dementia progression in amyloid‐negative amnestic MCI patients. [ABSTRACT FROM AUTHOR]