Radiomic Texture Analysis Mapping Predicts Areas of True Functional MRI Activity.

Individual analysis of functional Magnetic Resonance Imaging (fMRI) scans requires user-adjustment of the statistical threshold in order to maximize true functional activity and eliminate false positives. In this study, we propose a novel technique that uses radiomic texture analysis (TA) features associated with heterogeneity to predict areas of true functional activity. Scans of 15 right-handed healthy volunteers were analyzed using SPM8. The resulting functional maps were thresholded to optimize visualization of language areas, resulting in 116 regions of interests (ROIs). A board-certified neuroradiologist classified different ROIs into Expected (E) and Non-Expected (NE) based on their anatomical locations. TA was performed using the mean Echo-Planner Imaging (EPI) volume, and 20 rotation-invariant texture features were obtained for each ROI. Using forward stepwise logistic regression, we built a predictive model that discriminated between E and NE areas of functional activity, with a cross-validation AUC and success rate of 79.84% and 80.19% respectively (specificity/sensitivity of 78.34%/82.61%). This study found that radiomic TA of fMRI scans may allow for determination of areas of true functional activity, and thus eliminate clinician bias

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Differential Effect of Demographics, Processing Speed, and Depression on Cognitive Function in 755 Non-demented Community-dwelling Elderly Individuals

Background:Several factors may account for inter-and intra-individual variability in cognitive functions, including age, gender, education level, information processing speed, and mood.Objective:To evaluate the combined contribution of demographic factors, information processing speed, and depressive symptoms to scores on several diagnostic cognitive measures that are commonly used in geriatric neuropsychological practice in Greece.Methods:Using a cross-sectional study, we established a multivariate general linear model and analyzed the predictive role of age, gender, education, information processing speed (Trail Making Test-Part A), and depressive symptoms (Geriatric Depression Scale-15 Items) on measures of general cognitive status (Mini-Mental State Examination), verbal memory (Rey Auditory Verbal Learning Test), language (Confrontation Naming), and executive functions (Category and Phonemic Fluency, Trail Making Test-Part B) for a sample of 755 healthy, community-dwelling Greek individuals aged 50 to 90 years.Results:Participant factors significantly but differentially contributed to cognitive measures. Demographic factors and information processing speed emerged as the significant predictors for the majority of the cognitive measures (Mini-Mental State Examination; Rey Auditory Verbal Learning Test; Confrontation Naming; Category and Phonemic Fluency; Trail Making Test-Part B), whereas depressive symptoms significantly predicted verbal memory and semantic fluency measures (Rey Auditory Verbal Learning Test and Category Fluency).Conclusions:Clinicians should consider participant demographics, underlying slowing of processing speed, and depressive symptoms as potential confounding factors in cognitive measures. Our findings may explain the observed inter-and intra-individual variability in cognitive functions in the elderly population. © 2019 Wolters Kluwer Health, Inc. All rights reserved

Herpes simplex virus seroprevalence among children, adolescents and adults in Greece

The aim was to study the type-specific seroprevalence of Herpes simplex virus (HSV)-1 and HSV-2 infections and the associated risk factors in children, adolescents and adults in Greece. A total of 1867 serum samples from children, adolescents and adults of both genders aged from0 to 60 yearswere collected fromthree large hospital-referral centres in Athens. All serawere tested for type-specific antibodies to HSV-1 and HSV-2 using HerpeSelect IgG ELISA tests (Focus Diagnostics Cypress, Cal, USA). Overall ageadjusted seroprevalence of HSV-1 and HSV-2 was 72.0% and 10.2%, respectively. HSV-1 seropositivity was low in children up to nine years of age, increased sharply in adolescence, and was higher in females than males in each group surveyed. HSV-2 seropositivity was low(0.8%) below 20 years of age and increased to a maximum prevalence of 18.7% in men and 22.7% in women. Multivariate risk factor analyses indicated that HSV-1 seropositivity was associated with socioeconomic indicators (e.g. lower educational level, residency outside greater Athens), whereas HSV-2 was associated with sexual behavioural factors (e.g. being divorced, greater number of lifetime sexual partners). HSV-2 risk factor profiles were similar in women and in men. This first large seroprevalence study in Greece showed a high age-standardized HSV-1 seropositivity after adolescence and a relatively lowage-standardized HSV-2 seroprevalence frombirth to 60 years of age. Dual seropositivity to HSV-1 and HSV-2 was low (0.6%) in females under 20 years of age, suggesting that the potential use of an HSV-2 prophylactic vaccine in adolescents could reduce the spread of HSV-2 infection

Hemostasis parameters disturbances in healthy individuals with prehypertension

Prehypertension (PH) seems to be related to increased cardiovascular risk in healthy normotensive subjects, while essential hypertension is associated with hemostasis balance disturbances. The aim of our study was to examine the impact of PH on hemostasis parameters in healthy individuals with PH and to compare the findings with those of healthy normotensives with normal blood pressure (NBP) levels. This study was performed in 204 (96 M, 108 F) subjects who attended our hypertension clinic. Seventy-eight (36 M, 42 F) subjects with PH, mean age 52 ± 5 years, and body mass index (BMI) 23 ± 1.5 kg/m2 made up group A, and 126 (60 M, 66 F) subjects with NBP, mean age 53 ± 6 years, and BMI 23.2 ± 1.4 kg/m2 without any history of cardiovascular disease or diabetes mellitus made up group B. Systolic blood pressure and diastolic blood pressure were measured in three sequential visits, which were performed by the same trained nurse. Serum lipid levels, fibrinogen (F), thrombomodulin (TM), and plasminogen activator inhibitor-1 antigen, and tissue plasminogen activator antigen were determined in the whole population. Plasminogen activator inhibitor-1 antigen and tissue plasminogen activator antigen levels were significantly higher in the PH group as compared with normotensives, while in PH subjects, significantly higher plasma levels of F and TM were found compared with normotensive group. The two groups were matched for age, sex, BMI, and serum lipid levels. Our findings indicate that PH is associated with hemostasis disturbances predisposing to hypercoagulability and impaired fibrinolysis. This observation may be of prognostic value for future cardiovascular events in this group and needs further investigation. © 2012 Informa Healthcare USA, Inc