A deep look into radiomics.

Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

Development and validation of a simple algorithm to estimate common gestational age categories using standard administrative birth record data in Ontario, Canada.

Gestational age is often incompletely recorded in administrative records, despite being critical to paediatric and maternal health research. Several algorithms exist to estimate gestational age using administrative databases; however, many have not been validated or use complicated methods that are not readily adaptable. We developed a simple algorithm to estimate common gestational age categories from routine administrative data. We leveraged a population-based registry of all hospital births occurring in Ontario, Canada over 2002-2016 including 1.8 million birth records. In this sample, this simple algorithm had excellent performance compared to a verified measure of gestational age; 87.61% agreement (95% CI: 87.49, 87.74). The accuracy of the algorithm exceeded 98% for all of the gestational age categories. Agreement notably increased over time and was greatest among singleton births and infants born at 2500-2999 g. This study provides a straight-forward algorithm for accurately estimating common gestational age categories that is easily adaptable for use in other countries.Impact StatementWhat is already known on this subject? Gestational age is often incompletely or inaccurately recorded in administrative health databases, despite being critical to the study of many paediatric and maternal health outcomes. Consequently, researchers must rely on various methods to estimate gestational age, many of these methods are either overly simple (i.e. assuming a uniform duration) or analytically complicated and difficult to adapt for new populations (e.g. regression-based approaches).What the results of this study add? This study, based on a population-based registry of all 1.8 million births occurring in Ontario, Canada 2003-2016, found that a simple, sex-specific algorithm using three commonly recorded birth record characteristics performs almost perfectly compared to a clinical estimate recorded near birth.What the implications are of these findings for clinical practice and/or further research? This study suggests that a straight-forward, sex-specific algorithm based on routinely collected birth record data is able to accurately estimate common gestational age categories (i.e. extreme preterm, <28 weeks; very preterm, 28-32 weeks; moderate-to-late preterm, 33-26 weeks; and term, 37 weeks of completed gestational age). This work will be of greatest interest to perinatal researchers using routinely collected health administrative data.

Openness and trust in data-intensive science: the case of biocuration.

Data-intensive science comes with increased risks concerning quality and reliability of data, and while trust in science has traditionally been framed as a matter of scientists being expected to adhere to certain technical and moral norms for behaviour, emerging discourses of open science present openness and transparency as substitutes for established trust mechanisms. By ensuring access to all available information, quality becomes a matter of informed judgement by the users, and trust no longer seems necessary. This strategy does not, however, take into consideration the networks of professionals already enabling data-intensive science by providing high-quality data. In the life sciences, biological data- and knowledge bases managed by expert biocurators have become crucial for data-intensive research. In this paper, I will use the case of biocurators to argue that openness and transparency will not diminish the need for trust in data-intensive science. On the contrary, data-intensive science requires a reconfiguration of existing trust mechanisms in order to include those who take care of and manage scientific data after its production.

Multisite collaboration using REDCap to capture library data.

In January 2018, library services at Providence St. Joseph Health merged to form a single, unified system, incorporating nine libraries and sixteen full-time staff. As a small, nonclinical team of librarians, we needed to make sure our work and value were visible to clinicians, administrators, and other nonlibrary stakeholders. Using REDCap, we developed a form to seamlessly collect statistics about our services.

Cochrane's Linked Data Project: How it Can Advance our Understanding of Surrogate Endpoints.

Cochrane has developed a linked data infrastructure to make the evidence and data from its rich repositories more discoverable to facilitate evidence-based health decision-making. These annotated resources can enhance the study and understanding of biomarkers and surrogate endpoints.

Centralized Clinical Trial Imaging Data Management: Practical Guidance from a Comprehensive Cancer Center's Experience.

Medical imaging is an integral part of clinical trial research and it must be managed properly to provide accurate data to the sponsor in a timely manner (Clune in Cancer Inform 4:33-56, 2007; Wang et al. in Proc SPIE Int Soc Opt Eng 7967, 2011). Standardized workflows for site qualification, protocol preparation, data storage, retrieval, de-identification, submission, and query resolution are paramount to achieve quality clinical trial data management such as reducing the number of imaging protocol deviations and avoiding delays in data transfer. Centralization of data management and implementation of relational databases and electronic workflows can help maintain consistency and accuracy of imaging data. This technical note aims at sharing the practical implementation of our centralized clinical trial imaging data management processes to avoid the fragmentation of tasks among various disease centers and research staff, and enable us to provide quality, accurate, and timely imaging data to clinical trial sponsors.

A Review of Clinical Data Management Systems Used in Clinical Trials.

BACKGROUND: A clinical data management system is a software supporting the data management process in clinical trials. In this system, the effective support of clinical data management dimensions leads to the increased accuracy of results and prevention of diversion in clinical trials. The aim of this review article was to investigate the dimensions of data management in clinical data management systems. METHODS: This study was conducted in 2017. The used databases included Web of Science, Scopus, Science Direct, ProQuest, Ovid Medline and PubMed. The search was conducted over a period of 10 years from 2007 to 2017. The initial number of studies was 101 reaching 19 in the final stage. The final studies were described and compared in terms of the year, country and dimensions of the clinical data management process in clinical trials. RESULTS: The research findings indicated that none of the systems completely supported the data management dimensions in clinical trials. Although these systems were developed for supporting the clinical data management process, they were similar to electronic data capture systems in many cases. The most significant dimensions of data management in such systems were data collection or entry, report, validation, and security maintenance. CONCLUSION: Seemingly, not sufficient attention has been paid to automate all dimensions of the clinical data management process in clinical trials. However, these systems could take positive steps towards changing the manual processes of clinical data management to electronic processes.

Avaliação da implantação do Sistema de Controle, Acompanhamento e Avaliação de Resultados (Sistema e-Car) na Secretaria de Vigilância em Saúde do Ministério da Saúde do Brasil, no período de 2012 - 2015 / Evaluación de la implantación del Sistema de Control, Monitoreo y Evaluación de Resultados (Sistema e-Car) en la Secretaría de Vigilancia en Salud del Ministerio de Salud de Brasil, en el período de 2012 - 2015 / Evaluation of the Results Control, Monitoring and Evaluation System (e-Car system) Implementation at the Health Surveillance Secretariat, Brazilian Ministry of Health, 2012-2015

Objetivo: avaliar a implantação do Sistema de Controle, Acompanhamento e Avaliação de Resultados (e-Car) na Secretaria de Vigilância em Saúde (SVS) do Ministério da Saúde, no período de 2012 a 2015. Métodos: estudo de avaliação utilizando métodos mistos para a coleta de dados primários e secundários, seguido da definição do grau de implantação do sistema e da análise da influência do contexto político-organizacional. Resultados: o sistema e-Car foi considerado implantado na SVS, alcançando o percentual de 75,4%; as piores performances da dimensão de estrutura foram relacionadas ao serviço de manutenção dos computadores e do sistema operacional, bem como ao baixo conhecimento sobre o manual do sistema; na dimensão de processo, a existência de instâncias colegiadas foi considerada benéfica para o monitoramento. Conclusão: o sistema e-Car foi implantado na SVS; entretanto, sua sustentabilidade mostrou-se frágil, sendo recomendados a institucionalização e o fortalecimento das práticas de monitoramento e avaliação pela SVS.

Objetivo: evaluar la implantación del Sistema de Control, Monitoreo y Evaluación de Resultados (e-Car) en la Secretaría de Vigilancia en Salud (SVS) del Ministerio de Salud de Brasil, en el período de 2012 a 2015. Métodos: estudio de evaluación de métodos mixtos para la recolección de datos primarios y secundarios, seguido de la definición del grado de implantación del sistema y análisis de la influencia del contexto político-organizacional. Resultados: el sistema e-Car fue considerado implantado en la SVS, alcanzando un 75,4%; las peores actuaciones de la dimensión de estructura fueron el servicio de mantenimiento de las computadoras y del sistema operacional, así como el bajo conocimiento del manual del sistema; en la dimensión de proceso, la existencia de instancias colegiadas fue considerada benéfica para el monitoreo. Conclusión: el sistema e-Car fue implantado en la SVS; todavía, su sostenibilidad se mostró frágil, recomendándose la institucionalización y el fortalecimiento de las prácticas de monitoreo y evaluación por la SVS.

Objective: to evaluate the implementation of the Results Control, Monitoring and Evaluation System (e-Car) at the Health Surveillance Secretariat (SVS), Brazilian Ministry of Health, in the period 2012-2015. Methods: this was an evaluation study using mixed methods for collection of primary and secondary data, followed by definition of the system's degree of implementation and analysis of the influence of the political-organizational context. Results: the e-Car System was considered to have been implemented (75.4%) at SVS; the worst scores for its structure dimension related to the computer and operating system maintenance service, as well as little knowledge of the system manual; as for the system's process dimension, the existence of collegiate bodies was considered beneficial for monitoring. Conclusion: the e-Car System had been implemented at SVS; however, its sustainability was fragile, and SVS was recommended to institutionalize and strengthen its monitoring and evaluation practices.

[Opportunities and challenges of blockchain technology in the healthcare industry]. / Opportunités et enjeux de la technologie blockchain dans le secteur de la santé.

Often described as a tool to build trust among stakeholders with divergent interests, blockchain technology has been of interest to many sectors since it was first used in 2008. Initially designed to record financial transactions between individuals, its applications have largely evolved with technological advances and the growing interest of international companies. In the healthcare sector, blockchain is interesting for many of its features: its immutability which makes it an excellent support for authenticating sensitive data such as clinical trials consents, the possibility of publishing smart contracts that automate and facilitate many processes or the constitution of a network that agrees on the state of the information. Much acclaimed, blockchain technology is still to be tested in real-life conditions and adapted to a particularly complex regulatory and economic context in the healthcare sector.

Reference Managers that Support Collaborative Research: Dreaming of the Perfect Fit.

Reference or citation managers aid in capturing and managing citations and associated full text, tracking references and citing them properly in manuscripts, and creating bibliographies. With more features than ever, selecting the most appropriate reference manager can be overwhelming for users and librarians. One common situation in which librarians are asked for advice involves building shared libraries of references to support collaborative group work. This project developed a structured evaluation for comparison of several common citation managers and prototypical use cases to help match features with user needs, preferences, and workflows. As products evolve and needs change, is there a "perfect fit"?

How Heterogeneity Affects the Design of Hadoop MapReduce Schedulers: A State-of-the-Art Survey and Challenges.

MapReduce (MR) computing paradigm and its open source implementation Hadoop have become a de facto standard to process big data in a distributed environment. Initially, the Hadoop system was homogeneous in three significant aspects, namely, user, workload, and cluster (hardware). However, with growing variety of MR jobs and inclusion of different configurations of nodes in the existing cluster, heterogeneity has become an essential part of Hadoop systems. The heterogeneity factors adversely affect the performance of a Hadoop scheduler and limit the overall throughput of the system. To overcome this problem, various heterogeneous Hadoop schedulers have been proposed in the literature. Existing survey works in this area mostly cover homogeneous schedulers and classify them on the basis of quality of service parameters they optimize. Hence, there is a need to study the heterogeneous Hadoop schedulers on the basis of various heterogeneity factors considered by them. In this survey article, we first discuss different heterogeneity factors that typically exist in a Hadoop system and then explore various challenges that arise while designing the schedulers in the presence of such heterogeneity. Afterward, we present the comparative study of heterogeneous scheduling algorithms available in the literature and classify them by the previously said heterogeneity factors. Lastly, we investigate different methods and environment used for evaluation of discussed Hadoop schedulers.

Can Texas Do Death Better?

Filling out death certificates can be confusing for Texas physicians, and vital health information often gets left off of forms. Because of this, officials don't have accurate information on how Texans are dying.

An Architecture for Translational Cancer Research As Exemplified by the German Cancer Consortium.

Networking of medical institutions by means of a capable data infrastructure has the potential to open up vast amounts of routine data to translational cancer research. However, the secondary use of information collected independently in several institutions is a challenging task of data integration. In this review, we discuss the requirements and common challenges involved in the establishment of such a platform. We present methods and tools from the field of medical informatics as solutions to semantic and technical heterogeneity, questions of data protection and record linkage, as well as issues of trust and data ownership. We also describe the architecture of an existing cancer research network as an exemplary application of these methods.

Oak Ridge Reservation Environmental Protection Rad Neshaps Radionuclide Inventory Web Database and Rad Neshaps Source and Dose Database.

The U.S. Environmental Protection Agency promulgated national emission standards for emissions of radionuclides other than radon from US Department of Energy facilities in Chapter 40 of the Code of Federal Regulations (CFR) 61, Subpart H. This regulatory standard limits the annual effective dose that any member of the public can receive from Department of Energy facilities to 0.1 mSv. As defined in the preamble of the final rule, all of the facilities on the Oak Ridge Reservation, i.e., the Y-12 National Security Complex, Oak Ridge National Laboratory, East Tennessee Technology Park, and any other U.S. Department of Energy operations on Oak Ridge Reservation, combined, must meet the annual dose limit of 0.1 mSv. At Oak Ridge National Laboratory, there are monitored sources and numerous unmonitored sources. To maintain radiological source and inventory information for these unmonitored sources, e.g., laboratory hoods, equipment exhausts, and room exhausts not currently venting to monitored stacks on the Oak Ridge National Laboratory campus, the Environmental Protection Rad NESHAPs Inventory Web Database was developed. This database is updated annually and is used to compile emissions data for the annual Radionuclide National Emission Standards for Hazardous Air Pollutants (Rad NESHAPs) report required by 40 CFR 61.94. It also provides supporting documentation for facility compliance audits. In addition, a Rad NESHAPs source and dose database was developed to import the source and dose summary data from Clean Air Act Assessment Package-1988 computer model files. This database provides Oak Ridge Reservation and facility-specific source inventory; doses associated with each source and facility; and total doses for the Oak Ridge Reservation dose.

[Registries for rare diseases : OSSE - An open-source framework for technical implementation]. / Register für seltene Erkrankungen : OSSE ­ ein Open-Source-Framework für die technische Umsetzung.

Meager amounts of data stored locally, a small number of experts, and a broad spectrum of technological solutions incompatible with each other characterize the landscape of registries for rare diseases in Germany. Hence, the free software Open Source Registry for Rare Diseases (OSSE) was created to unify and streamline the process of establishing specific rare disease patient registries. The data to be collected is specified based on metadata descriptions within the registry framework's so-called metadata repository (MDR), which was developed according to the ISO/IEC 11179 standard. The use of a central MDR allows for sharing the same data elements across any number of registries, thus providing a technical prerequisite for making data comparable and mergeable between registries and promoting interoperability.With OSSE, the foundation is laid to operate linked patient registries while respecting strong data protection regulations. Using the federated search feature, data for clinical studies can be identified across registries. Data integrity, however, remains intact since no actual data leaves the premises without the owner's consent. Additionally, registry solutions other than OSSE can participate via the OSSE bridgehead, which acts as a translator between OSSE registry networks and non-OSSE registries. The pseudonymization service Mainzelliste adds further data protection.Currently, more than 10 installations are under construction in clinical environments (including university hospitals in Frankfurt, Hamburg, Freiburg and Münster). The feedback given by the users will influence further development of OSSE. As an example, the installation process of the registry for undiagnosed patients at University Hospital Frankfurt is described in more detail.

Development of multi-disciplinary breast cancer care in Southern Malawi.

The Edinburgh Malawi Breast Cancer Project, a collaborative partnership project between the Queen Elizabeth Central Hospital (QECH) Oncology Unit, Blantyre, Malawi and the Edinburgh Cancer Centre, UK, was established in 2015. The principal objective of the project is to help to develop high quality multi-disciplinary breast cancer care in Malawi. A needs assessment identified three priority areas for further improvement of breast cancer services: multi-disciplinary working, development of oestrogen receptor (ER) testing and management of clinical data. A 3-year project plan was implemented which has been conducted through a series of reciprocal training visits. Key achievements to date have been: (1) Development of a new specialist breast care nursing role; (2) Development of multi-disciplinary meetings; (3) Completion of a programme of oncology nursing education; (4) Development of a clinical database that enables prospective collection of data of all new patients with breast cancer; (5) Training of local staff in molecular and conventional approaches to ER testing. The Edinburgh Malawi Breast Cancer Project is supporting nursing education, data use and cross-specialty collaboration that we are confident will improve cancer care in Malawi. Future work will include the development of a breast cancer diagnostic clinic and a breast cancer registry.

Portuguese Haemophilia Registry. Set of variables for a computerized solution.

National Patient Registries (NPR) have an important role in the management of haemophilia and other inherited bleeding disorders, representing powerful instruments to support healthcare and research. Computer software to assist the NPR is crucial, as it facilitates the introduction of the data from a national universe that will be centralized and merged into a unique location, thus ensuring a greater reliability and accuracy of the collected data, avoiding duplication of patients. In Portugal, despite the efforts and recognition of the need of a NPR, just recently the protocol for the establishment of the computer software to support the Portuguese National Registry of Haemophilia and other Congenital Coagulopathies (PorR H&CC) was approved. This paper aims to present this newly developed computerized solution, as well as to report the main variables and information that will be available. The development of this application, which includes a set of socio-demographic, clinical and treatment data, was based on the principles of WFH, and the database that supports the NPR, with anonymized data, is operated and maintained in accordance with the Data Protection Law. Currently, the first data are available on the application. Our focus now is to ensure more registries and continuous data entry in order to have complete information on the characterization of the haemophilia patient population in Portugal.

A vision for collaborative training infrastructure for bioinformatics.

In biology, a missing link connecting data generation and data-driven discovery is the training that prepares researchers to effectively manage and analyze data. National and international cyberinfrastructure along with evolving private sector resources place biologists and students within reach of the tools needed for data-intensive biology, but training is still required to make effective use of them. In this concept paper, we review a number of opportunities and challenges that can inform the creation of a national bioinformatics training infrastructure capable of servicing the large number of emerging and existing life scientists. While college curricula are slower to adapt, grassroots startup-spirited organizations, such as Software and Data Carpentry, have made impressive inroads in training on the best practices of software use, development, and data analysis. Given the transformative potential of biology and medicine as full-fledged data sciences, more support is needed to organize, amplify, and assess these efforts and their impacts.

Can data repositories help find effective treatments for complex diseases?

There are many challenges to developing treatments for complex diseases. This review explores the question of whether it is possible to imagine a data repository that would increase the pace of understanding complex diseases sufficiently well to facilitate the development of effective treatments. First, consideration is given to the amount of data that might be needed for such a data repository and whether the existing data storage infrastructure is enough. Several successful data repositories are then examined to see if they have common characteristics. An area of science where unsuccessful attempts to develop a data infrastructure is then described to see what lessons could be learned for a data repository devoted to complex disease. Then, a variety of issues related to sharing data are discussed. In some of these areas, it is reasonably clear how to move forward. In other areas, there are significant open questions that need to be addressed by all data repositories. Using that baseline information, the question of whether data archives can be effective in understanding a complex disease is explored. The major goal of such a data archive is likely to be identifying biomarkers that define sub-populations of the disease.

Unified Database for Rejected Image Analysis Across Multiple Vendors in Radiography.

Reject rate analysis has been part of radiography departments' quality control since the days of screen-film radiography. In the era of digital radiography, one might expect that reject rate analysis is easily facilitated because of readily available information produced by the modality during the examination procedure. Unfortunately, this is not always the case. The lack of an industry standard and the wide variety of system log entries and formats have made it difficult to implement a robust multivendor reject analysis program, and logs do not always include all relevant information. The increased use of digital detectors exacerbates this problem because of higher reject rates associated with digital radiography compared with computed radiography. In this article, the authors report on the development of a unified database for vendor-neutral reject analysis across multiple sites within an academic institution and share their experience from a team-based approach to reduce reject rates.