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

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

Gender discrepancy in research activities during radiology residency

Vernuccio, Federica/0000-0003-0350-1794; Arzanauskaite, Monika/0000-0002-4004-4150; Shelmerdine, Susan/0000-0001-6642-9967WOS: 000503697500001PubMed: 31865450Objective To investigate the presence of gender disparity in academic involvement during radiology residency and to identify and characterize any gender differences in perceived barriers for conducting research. Methods An international call for participation in an online survey was promoted via social media and through multiple international and national radiological societies. A 35-question survey invited radiology trainees worldwide to answer questions regarding exposure and barriers to academic radiology during their training. Gender differences in response proportions were analyzed using either Fisher's exact or chi-squared tests. Results Eight hundred fifty-eight participants (438 men, 420 women) from Europe (432), Asia (241), North and South America (144), Africa (37), and Oceania (4) completed the survey. Fewer women radiology residents were involved in research during residency (44.3%, 186/420 vs 59.4%, 260/438; p <= 0.0001) and had fewer published original articles (27.9%, 117/420 vs. 40.2%, 176/438; p = 0.001). Women were more likely to declare gender as a barrier to research (24.3%, 102/420 vs. 6.8%, 30/438; p < 0.0001) and lacked mentorship/support from faculty (65%, 273/420 vs. 55.7%, 244/438; p = 0.0055). Men were more likely to declare a lack of time (60.3%, 264/438 vs. 50.7%, 213/420; p = 0.0049) and lack of personal interest (21%, 92/438 vs. 13.6%, 57/420, p = 0.0041) in conducting research. Conclusion Fewer women were involved in academic activities during radiology residency, resulting in fewer original published studies compared to their men counterparts. This is indicative of an inherent gender imbalance. Lack of mentorship reported by women radiologists was a main barrier to research

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T).

Aim: The aim of this study was to determine the prevalence of childhood psychopathologies in Turkey

The prevalence of childhood psychopathology in Turkey: a cross-sectional multicenter nationwide study (EPICPAT-T)

Conclusion: This is the largest and most comprehensive epidemiological study to determine the prevalence of psychopathologies in children and adolescents in Turkey. Our results partly higher than, and partly comparable to previous national and international studies. It also contributes to the literature by determining the independent predictors of psychopathologies in this age group

Prevalence of Childhood Affective disorders in Turkey: An epidemiological study

Aim: To determine the prevalence of affective disorders in Turkey among a representative sample of Turkish population. Methods: This study was conducted as a part of the "The Epidemiology of Childhood Psychopathology in Turkey" (EPICPAT-T) Study, which was designed by the Turkish Association of Child and Adolescent Mental Health. The inclusion criterion was being a student between the second and fourth grades in the schools assigned as study centers. The assessment tools used were the K-SADS-PL, and a sociodemographic form that was designed by the authors. Impairment was assessed via a 3 point-Likert type scale independently rated by a parent and a teacher. Results: A total of 5842 participants were included in the analyses. The prevalence of affective disorders was 2.5 % without considering impairment and 1.6 % when impairment was taken into account. In our sample, the diagnosis of bipolar disorder was lacking, thus depressive disorders constituted all the cases. Among depressive disorders with impairment, major depressive disorder (MDD) (prevalence of 1.06%) was the most common, followed by dysthymia (prevalence of 0.2%), adjustment disorder with depressive features (prevalence of 0.17%), and depressive disorder-NOS (prevalence of 0.14%). There were no statistically significant gender differences for depression. Maternal psychopathology and paternal physical illness were predictors of affective disorders with pervasive impairment. Conclusion: MDD was the most common depressive disorder among Turkish children in this nationwide epidemiological study. This highlights the severe nature of depression and the importance of early interventions. Populations with maternal psychopathology and paternal physical illness may be the most appropriate targets for interventions to prevent and treat depression in children and adolescents