analysis of brain nmr images for age estimation with deep learning

Abstract During the last decade, deep learning and Convolutional Neural Networks (CNNs) have produced a devastating impact on computer vision, yielding exceptional results on a variety of problems, including analysis of medical images. Recently, these techniques have been extended to 3D images with the downside of a large increase in the computational load. In particular, state-of-the-art CNNs have been used for brain Nuclear Magnetic Resonance (NMR) imaging, with the aim of estimating the patients' age. In fact, a large discrepancy between the real and the estimated age is a clear alarm for the onset of neurodegenerative diseases, such as some types of early dementia and Alzheimer's disease. In this paper, we propose an effective alternative to 3D convolutions that guarantees a significant reduction of the computational requirements for this kind of analysis. The proposed architectures achieve comparable results with the competitor 3D methods, requiring only a fraction of the training time and GPU memory

The epidemiology of malignant mesothelioma in women: gender differences and modalities of asbestos exposure

INTRODUCTION: The epidemiology of gender differences for mesothelioma incidence has been rarely discussed in national case lists. In Italy an epidemiological surveillance system (ReNaM) is working by the means of a national register. METHODS: Incident malignant mesothelioma (MM) cases in the period 1993 to 2012 were retrieved from ReNaM. Gender ratio by age class, period of diagnosis, diagnostic certainty, morphology and modalities of asbestos exposure has been analysed using exact tests for proportion. Economic activity sectors, jobs and territorial distribution of mesothelioma cases in women have been described and discussed. To perform international comparative analyses, the gender ratio of mesothelioma deaths was calculated by country from the WHO database and the correlation with the mortality rates estimated. RESULTS: In the period of study a case list of 21 463 MMs has been registered and the modalities of asbestos exposure have been investigated for 16 458 (76.7%) of them. The gender ratio (F/M) was 0.38 and 0.70 (0.14 and 0.30 for occupationally exposed subjects only) for pleural and peritoneal cases respectively. Occupational exposures for female MM cases occurred in the chemical and plastic industry, and mainly in the non-asbestos textile sector. Gender ratio proved to be inversely correlated with mortality rate among countries. CONCLUSIONS: The consistent proportion of mesothelioma cases in women in Italy is mainly due to the relevant role of non-occupational asbestos exposures and the historical presence of the female workforce in several industrial settings. Enhancing the awareness of mesothelioma aetiology in women could support the effectiveness of welfare system and prevention policies

The epidemiology of malignant mesothelioma in women: gender differences and modalities of asbestos exposure

ntroduction The epidemiology of gender differences for mesothelioma incidence has been rarely discussed in national case lists. In Italy an epidemiological surveillance system (ReNaM) is working by the means of a national register. Methods Incident malignant mesothelioma (MM) cases in the period 1993 to 2012 were retrieved from ReNaM. Gender ratio by age class, period of diagnosis, diagnostic certainty, morphology and modalities of asbestos exposure has been analysed using exact tests for proportion. Economic activity sectors, jobs and territorial distribution of mesothelioma cases in women have been described and discussed. To perform international comparative analyses, the gender ratio of mesothelioma deaths was calculated by country from the WHO database and the correlation with the mortality rates estimated. Results In the period of study a case list of 21 463 MMs has been registered and the modalities of asbestos exposure have been investigated for 16 458 (76.7%) of them. The gender ratio (F/M) was 0.38 and 0.70 (0.14 and 0.30 for occupationally exposed subjects only) for pleural and peritoneal cases respectively. Occupational exposures for female MM cases occurred in the chemical and plastic industry, and mainly in the non-asbestos textile sector. Gender ratio proved to be inversely correlated with mortality rate among countries. Conclusions The consistent proportion of mesothelioma cases in women in Italy is mainly due to the relevant role of non-occupational asbestos exposures and the historical presence of the female workforce in several industrial settings. Enhancing the awareness of mesothelioma aetiology in women could support the effectiveness of welfare system and prevention policie

Letter concerning:‘Response to:‘The epidemiology of malignant mesothelioma in women: gender differences and modalities of asbestos exposure’by Marinaccio et al’

Finkelstein1 invited physicians and researchers interested in mesothelioma to investigate on past usage of talcum powders by affected people. In Italy, asbestos contamination in talc for industrial use has been documented,2 and, as he underlines tremolite contamination at low levels of cosmetic and pharmaceutical talc has been reported in USA by Blount3 and Gordon and colleagues.4 In the Italian National Mesothelioma Register (ReNaM), the analysis of intensive exposure to talc has been evaluated with respect to occupational and environmental history. The catalogue of possible asbestos exposure circumstances (a tool for the interviewers) reports the potential presence of industrial talcs in quarries or mines working activities, in leather tanning and in rubber industries. The use of intensive cosmetic talc for personal use is evaluated by means of a structured questionnaire,5 as reported in the ReNaM guidelines (see https://www.inail.it/cs/internet/docs/all-linee-guida-renam.pdf?section=attivita, p82, p98, in Italian). In our paper regarding gender differences in mesothelioma epidemiology,6 we have presented figures referring to 21 463 MM cases detected by ReNaM with a diagnosis between 1993 and 2012. Among female case list (6087 cases), 4374 cases (71.9%) have been interviewed for defining exposure. During the interview, 30 MM female cases referred an intensive use of talc in the context of occupational or life habits. For five of them, the regional centre has identified an exposure to asbestos due to intensive talc use, classifying such modality of exposure as ‘leisure activities’ (see ReNaM guidelines5). For the remaining 25 cases, an occupational exposure to asbestos in other working (or familiar or environmental) circumstances has been identified and coded. Registry data such as those provided by ReNaM cannot provide estimates of the mesothelioma risk associated with any particular exposure circumstance. We plan to include talc exposure at work and cosmetic talc usage in the analyses of a case–control study on pleural mesothelioma currently under way. A specific survey to compare and discuss how the modalities of exposure to talc have been evaluated in patients with mesothelioma in countries where epidemiological surveillance systems are active could improve knowledge and support prevention policies

Graph neural networks for communication networks: context, use cases and opportunities

Graph neural networks (GNN) have shown outstanding applications in fields where data is essentially represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g., topology, routing, signal interference). This position article presents GNNs as a fundamental tool for modeling, control and management of communication networks. GNNs represent a new generation of data-driven models that can accurately learn and reproduce the complex behaviors behind real-world networks. As a result, these models can be applied to a wide variety of networking use cases, such as planning, online optimization, or troubleshooting. The main advantage of GNNs over traditional neural networks lies in their unprecedented generalization capabilities when applied to other networks and configurations unseen during training. This is a critical feature for achieving practical data-driven solutions for networking. This article starts with a brief tutorial on GNNs and some potential applications to communication networks. Then, it presents two state-of-the-art GNN models respectively applied to wired and wireless networks. Lastly, it delves into the key open challenges and opportunities yet to be explored in this novel research area.This publication is part of the Spanish I+D+i project TRAINER-A (ref. PID2020-118011GB-C21), funded by MCIN/AEI/10.13039/501100011033. This work is also partially funded by the Catalan Institution for Research and Advanced Studies (ICREA) and the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund.Peer ReviewedPostprint (author's final draft

Mesothelioma incidence surveillance systems and claims for workers’ compensation. Epidemiological evidence and prospects for an integrated framework

<p>Abstract</p> <p>Background</p> <p>Malignant mesothelioma is an aggressive and lethal tumour strongly associated with exposure to asbestos (mainly occupational). In Italy a large proportion of workers are protected from occupational diseases by public insurance and an epidemiological surveillance system for incident mesothelioma cases.</p> <p>Methods</p> <p>We set up an individual linkage between the Italian national mesothelioma register (ReNaM) and the Italian workers’ compensation authority (INAIL) archives. Logistic regression models were used to identify and test explanatory variables.</p> <p>Results</p> <p>We extracted 3270 mesothelioma cases with occupational origins from the ReNaM, matching them with 1625 subjects in INAIL (49.7%); 91.2% (1,482) of the claims received compensation. The risk of not seeking compensation is significantly higher for women and the elderly. Claims have increased significantly in recent years and there is a clear geographical gradient (northern and more developed regions having higher claims rates). The highest rates of compensation claims were after work known to involve asbestos.</p> <p>Conclusions</p> <p>Our data illustrate the importance of documentation and dissemination of all asbestos exposure modalities. Strategies focused on structural and systematic interaction between epidemiological surveillance and insurance systems are needed.</p

Malignant mesothelioma due to non-occupational asbestos exposure from the Italian national surveillance system (ReNaM): epidemiology and public health issues

Italy produced and imported a large amount of raw asbestos, up to the ban in 1992, with a peak in the period between 1976 and 1980 at about 160\u2005000 tons/year. The National Register of Mesotheliomas (ReNaM, "Registro Nazionale dei Mesoteliomi" in Italian), a surveillance system of mesothelioma incidence, has been active since 2002, operating through a regional structure

Epidemiological patterns of asbestos exposure and spatial clusters of incident cases of malignant mesothelioma from the Italian national registry.

BACKGROUND: Previous ecological spatial studies of malignant mesothelioma cases, mostly based on mortality data, lack reliable data on individual exposure to asbestos, thus failing to assess the contribution of different occupational and environmental sources in the determination of risk excess in specific areas. This study aims to identify territorial clusters of malignant mesothelioma through a Bayesian spatial analysis and to characterize them by the integrated use of asbestos exposure information retrieved from the Italian national mesothelioma registry (ReNaM). METHODS: In the period 1993 to 2008, 15,322 incident cases of all-site malignant mesothelioma were recorded and 11,852 occupational, residential and familial histories were obtained by individual interviews. Observed cases were assigned to the municipality of residence at the time of diagnosis and compared to those expected based on the age-specific rates of the respective geographical area. A spatial cluster analysis was performed for each area applying a Bayesian hierarchical model. Information about modalities and economic sectors of asbestos exposure was analyzed for each cluster. RESULTS: Thirty-two clusters of malignant mesothelioma were identified and characterized using the exposure data. Asbestos cement manufacturing industries and shipbuilding and repair facilities represented the main sources of asbestos exposure, but a major contribution to asbestos exposure was also provided by sectors with no direct use of asbestos, such as non-asbestos textile industries, metal engineering and construction. A high proportion of cases with environmental exposure was found in clusters where asbestos cement plants were located or a natural source of asbestos (or asbestos-like) fibers was identifiable. Differences in type and sources of exposure can also explain the varying percentage of cases occurring in women among clusters. CONCLUSIONS: Our study demonstrates shared exposure patterns in territorial clusters of malignant mesothelioma due to single or multiple industrial sources, with major implications for public health policies, health surveillance, compensation procedures and site remediation programs