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The therapeutic benefits of bilateral capsulotomy for the treatment of refractory obsessive compulsive disorder (OCD) are probably attributed to interruption of the cortico-striato-thalamo-cortical circuitry. We evaluated resting brain metabolism and treatment response in OCD patients using positron emission tomography (PET) imaging. [18F]-fluoro-deoxy-glucose PET was performed in eight OCD patients precapsulotomy and postcapsulotomy. We determined metabolic differences between preoperative images in patients and those in eight age-matched healthy volunteers, and postoperative changes and clinical correlations in the patients. The OCD patients showed widespread metabolic increases in normalized glucose metabolism in the bilateral orbitofrontal cortex and inferior frontal gyrus, cingulate gyrus, and bilateral pons/cerebellum, and metabolic decreases bilaterally in the precentral and lingual gyri. Bilateral capsulotomy resulted in significant metabolic decreases bilaterally in the prefrontal cortical regions, especially in the dorsal anterior cingulate cortex (ACC) and in the medial dorsal thalamus and caudate nucleus. In contrast, metabolism increased bilaterally in the precentral and lingual gyri. Clinical improvement in patients correlated with metabolic changes in the bilateral dorsal ACC and in the right middle occipital gyrus after capsulotomy. This study underscores the importance of the internal capsule in modulating ventral prefrontal and dorsal anterior cingulate neuronal activity in the neurosurgical management of OCD patients. [ABSTRACT FROM AUTHOR]

Purpose: We evaluated the relationship between isocitrate dehydrogenase 1 (IDH1) mutation status and metabolic imaging in patients with nonenhancing supratentorial diffuse gliomas using 11C-methionine positron emission tomography (11C-MET PET). Materials and Methods: Between June 2012 and November 2017, we enrolled 86 (38 women and 48 men; mean age, 41.9 ± 13.1 years [range, 8-67 years]) patients with newly diagnosed supratentorial diffuse gliomas. All patients underwent preoperative 11C-MET PET. Tumor samples were obtained and immunohistochemically analyzed for IDH1 mutation status. Results: The mutant and wild-type IDH1 diffuse gliomas had significantly different mean maximum standardized uptake value values (2.73 [95% confidence interval, CI: 2.32-3.16] vs 3.85 [95% CI: 3.22-4.51], respectively; P =.004) and mean tumor-to-background ratio (1.90 [95% CI: 1.65-2.16] vs 2.59 [95% CI: 2.17-3.04], respectively; P =.007). Conclusions: 11C-methionine PET can noninvasively evaluate the IDH1 mutation status of patients with nonenhancing supratentorial diffuse gliomas. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the most common form of progressive and irreversible dementia, and accurate diagnosis of AD at its prodromal stage is clinically important. Currently, computer-aided diagnosis of AD and mild cognitive impairment (MCI) using 18F-fluorodeoxy-glucose positron emission tomography (18F-FDG PET) imaging is usually based on low-level imaging features or deep learning methods, which have difficulties in achieving sufficient classification accuracy or lack clinical significance. This research therefore aimed to implement a new feature extraction method known as radiomics, to improve the classification accuracy and discover high-order features that can reveal pathological information. Methods: In this study, 18F-FDG PET and clinical assessments were collected in a cohort of 422 individuals [including 130 with AD, 130 with MCI, and 162 healthy controls (HCs)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 44 individuals (including 22 with AD, and 22 HCs) from Huashan Hospital, Shanghai, China. First, we performed a group comparison using a two-sample Student's t test to determine the regions of interest (ROIs) based on 30 AD patients and 30 HCs from ADNI cohorts. Second, based on two time scans of 32 HCs from ADNI cohorts, we used Cronbach's alpha coefficient for radiomic feature stability analyses. Pearson's correlation coefficients were regarded as a feature selection criterion, to select effective features associated with the clinical cognitive scale [clinical dementia rating scale in its sum of boxes (CDRSB); Alzheimer's disease assessment scale (ADAS)] with 500-times cross-validation. Finally, a support vector machine (SVM) was used to test the ability of the radiomic features to classify HCs, MCI and AD patients. Results: As a result, we identified brain regions which were mainly distributed in the temporal, occipital and frontal areas as ROIs. A total of 168 radiomic features of AD were stable (alpha > 0.8). The classification experiment led to maximal accuracies of 91.5%, 83.1% and 85.9% for classifying AD versus HC, MCI versus HCs and AD versus MCI. Conclusion: The research in this paper proved that the novel approach based on high-order radiomic features extracted from 18F-FDG PET brain images that can be used for AD and MCI computer-aided diagnosis. [ABSTRACT FROM AUTHOR]