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Cognitive impairment and psychiatric symptoms are common in patients with Cushing's disease (CD) owing to elevated levels of glucocorticoids. Molecular neuroimaging methods may help to detect changes in the brain of patients with CD. The aim of this study was to investigate the characteristics of brain metabolism and its association with serum cortisol level in CD. We compared brain metabolism, as measured using [18F]-fluorodeoxyglucose positron emission tomography (FDG PET), between 92 patients with CD and 118 normal subjects on a voxel-wise basis. Pearson correlation was performed to evaluate the association between cerebral FDG uptake and serum cortisol level in patients with CD. We demonstrated that certain brain regions in patients with CD showed significantly increased FDG uptake, including the basal ganglia, anteromedial temporal lobe, thalamus, precentral cortex, and cerebellum. The clusters that demonstrated significantly decreased uptake were mainly located in the medial and lateral frontal cortex, superior and inferior parietal lobule, medial occipital cortex, and insular cortex. The metabolic rate of the majority of these regions was found to be significantly correlated with the serum cortisol level. Our findings may help to explain the underlying mechanisms of cognitive impairment and psychiatric symptoms in patients exposed to excessive glucocorticoids and evaluate the efficacy of treatments during follow-up. Keywords: Cushing's disease, Positron emission tomography, Cortisol, Voxel-based analysis

Objective: The aim of this study was to develop a radiomics signature for prediction of progression-free survival (PFS) in lower-grade gliomas and to investigate the genetic background behind the radiomics signature. Methods: In this retrospective study, training (n = 216) and validation (n = 84) cohorts were collected from the Chinese Glioma Genome Atlas and the Cancer Genome Atlas, respectively. For each patient, a total of 431 radiomics features were extracted from preoperative T2-weighted magnetic resonance images. A radiomics signature was generated in the training cohort, and its prognostic value was evaluated in both the training and validation cohorts. The genetic characteristics of the group with high-risk scores were identified by radiogenomic analysis, and a nomogram was established for prediction of PFS. Results: There was a significant association between the radiomics signature (including 9 screened radiomics features) and PFS, which was independent of other clinicopathologic factors in both the training (P

Background: P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Methods: Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n=180) or validation (n=92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets. Results: The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. Conclusions: These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis. Keywords: p53, Lower-grade gliomas, Radiogenomics, Prediction, Machine learning

Chronic exposure to elevated levels of glucocorticoids can exert a neurotoxic effect in patients, possibly manifesting as molecular imaging alterations in patients. The aim of this study was to investigate the potential association between brain metabolism and elevated hormone level using 18F-fluorodeoxyglucose positron emission tomography. We retrospectively enrolled 92 consecutive patients with confirmed diagnosis of Cushing's disease. A voxel-based analysis was performed to investigate the association between cerebral 18F-fluorodeoxyglucose uptake and serum cortisol level. Relatively impaired metabolism of specific brain regions correlated with serum cortisol level was found. Specifically, notable correlations were found in the hippocampus, amygdala, and cerebellum, regions considered to be involved in the regulation and central action of glucocorticoids. Moreover, some hormone-associated regions were found in the frontal and occipital cortex, possibly mediating the cognitive changes seen in Cushing's disease. Our findings link patterns of perturbed brain metabolism relates to individual hormone level, thus presenting a substrate for cognitive disturbances seen in Cushing's disease patients, as well as in other conditions with abnormal cortisol levels.