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.

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.

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"?

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.

[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.

BBMRI-ERIC: the novel gateway to biobanks. From humans to humans.

BBMRI-ERIC, the Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium, is a new form of umbrella organization for biobanking in Europe. For rare and common diseases alike, it aims at providing fair access to quality-controlled human biological samples and associated biomedical and biomolecular data. Such access enables basic mechanisms underlying diseases to be studied, which is indispensable for the development of new biomarkers and drugs. In the context of the European Research Area (ERA), biobanks, which were identified as a particular European strength, contribute to Europe's cohesion policy through capacity-building in the BBMRI-ERIC member countries.

Genetics, lifestyle and environment. UK Biobank is an open access resource following the lives of 500,000 participants to improve the health of future generations.

UK Biobank is a long-term prospective epidemiology study having recruited and now following the lives of 500,000 people in England, Scotland and Wales, aged 40-69 years when they joined the study (Sudlow et al., PLoS Med 12(3):e1001779, 2015). Participants were recruited by letter and asked to attend one of 22 assessment centres in towns and cities across Britain, where they provided consent, answered detailed questions about their health and lifestyle, had body measures taken and donated blood, urine and saliva. Participants provided consent for the long-term follow-up of their health via medical records, such as general practice and hospital records, cancer and death records. Samples are being stored long term for a wide range of analyses, including genetic. The resource is open to all bona fide scientists from the UK and overseas, academic and industry who register via its access management system. Summary of UK Biobank data can be viewed via its Data Showcase and the resource will be strengthened over time as the results of new analyses and studies are returned, health links and participants provide additional information about themselves. Some will attend full repeat assessment visits. UK Biobank is open for business, and it hopes researchers will find it a valuable tool to improve the health of future generations.

[Strategies for biobank networks. Classification of different approaches for locating samples and an outlook on the future within the BBMRI-ERIC]. / Strategien zur Vernetzung von Biobanken. Klassifizierung verschiedener Ansätze zur Probensuche und Ausblick auf die Zukunft in der BBMRI-ERIC.

BACKGROUND: Medical research projects often require more biological material than can be supplied by a single biobank. For this reason, a multitude of strategies support locating potential research partners with matching material without requiring centralization of sample storage. OBJECTIVES: Classification of different strategies for biobank networks, in particular for locating suitable samples. Description of an IT infrastructure combining these strategies. MATERIALS AND METHODS: Existing strategies can be classified according to three criteria: (a) granularity of sample data: coarse bank-level data (catalogue) vs. fine-granular sample-level data, (b) location of sample data: central (central search service) vs. decentral storage (federated search services), and (c) level of automation: automatic (query-based, federated search service) vs. semi-automatic (inquiry-based, decentral search). All mentioned search services require data integration. Metadata help to overcome semantic heterogeneity. RESULTS: The "Common Service IT" in BBMRI-ERIC (Biobanking and BioMolecular Resources Research Infrastructure) unites a catalogue, the decentral search and metadata in an integrated platform. As a result, researchers receive versatile tools to search suitable biomaterial, while biobanks retain a high degree of data sovereignty. CONCLUSIONS: Despite their differences, the presented strategies for biobank networks do not rule each other out but can complement and even benefit from each other.

[Connecting biobanks of large European cohorts (EU Project BBMRI-LPC)]. / Vernetzung von Biobanken großer europäischer Kohorten (EU-Projekt BBMRI-LPC).

BACKGROUND: In addition to the Biobanking and BioMolecular resources Research Initiative (BBMRI), which is establishing a European research infrastructure for biobanks, a network for large European prospective cohorts (LPC) is being built to facilitate transnational research into important groups of diseases and health care. One instrument for this is the database "LPC Catalogue," which supports access to the biomaterials of the participating cohorts. OBJECTIVES: To present the LPC Catalogue as a relevant tool for connecting European biobanks. In addition, the LPC Catalogue has been extended to establish compatibility with existing Minimum Information About Biobank data Sharing (MIABIS) and to allow for more detailed search requests. This article describes the LPC Catalogue, its organizational and technical structure, and the aforementioned extensions. MATERIALS AND METHODS: The LPC Catalogue provides a structured overview of the participating LPCs. It offers various retrieval possibilities and a search function. To support more detailed search requests, a new module has been developed, called a "data cube". The provision of data by the cohorts is being supported by a "connector" component. RESULTS: The LPC Catalogue contains data on 22 cohorts and more than 3.8 million biosamples. At present, data on the biosamples of three cohorts have been acquired for the "cube," which is continuously being expanded. In the BBMRI-LPC, tendering for scientific projects using the data and samples of the participating cohorts is currently being carried out. In this context, several proposals have already been approved. CONCLUSIONS: The LPC Catalogue is supporting transnational access to biosamples. A comparison with existing solutions illustrates the relevance of its functionality.

Toward a complete dataset of drug-drug interaction information from publicly available sources.

Although potential drug-drug interactions (PDDIs) are a significant source of preventable drug-related harm, there is currently no single complete source of PDDI information. In the current study, all publically available sources of PDDI information that could be identified using a comprehensive and broad search were combined into a single dataset. The combined dataset merged fourteen different sources including 5 clinically-oriented information sources, 4 Natural Language Processing (NLP) Corpora, and 5 Bioinformatics/Pharmacovigilance information sources. As a comprehensive PDDI source, the merged dataset might benefit the pharmacovigilance text mining community by making it possible to compare the representativeness of NLP corpora for PDDI text extraction tasks, and specifying elements that can be useful for future PDDI extraction purposes. An analysis of the overlap between and across the data sources showed that there was little overlap. Even comprehensive PDDI lists such as DrugBank, KEGG, and the NDF-RT had less than 50% overlap with each other. Moreover, all of the comprehensive lists had incomplete coverage of two data sources that focus on PDDIs of interest in most clinical settings. Based on this information, we think that systems that provide access to the comprehensive lists, such as APIs into RxNorm, should be careful to inform users that the lists may be incomplete with respect to PDDIs that drug experts suggest clinicians be aware of. In spite of the low degree of overlap, several dozen cases were identified where PDDI information provided in drug product labeling might be augmented by the merged dataset. Moreover, the combined dataset was also shown to improve the performance of an existing PDDI NLP pipeline and a recently published PDDI pharmacovigilance protocol. Future work will focus on improvement of the methods for mapping between PDDI information sources, identifying methods to improve the use of the merged dataset in PDDI NLP algorithms, integrating high-quality PDDI information from the merged dataset into Wikidata, and making the combined dataset accessible as Semantic Web Linked Data.

DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.

BACKGROUND: Knowledge Organization Systems (KOS) and their associated mappings play a central role in several decision support systems. However, by virtue of knowledge evolution, KOS entities are modified over time, impacting mappings and potentially turning them invalid. This requires semi-automatic methods to maintain such semantic correspondences up-to-date at KOS evolution time. METHODS: We define a complete and original framework based on formal heuristics that drives the adaptation of KOS mappings. Our approach takes into account the definition of established mappings, the evolution of KOS and the possible changes that can be applied to mappings. This study experimentally evaluates the proposed heuristics and the entire framework on realistic case studies borrowed from the biomedical domain, using official mappings between several biomedical KOSs. RESULTS: We demonstrate the overall performance of the approach over biomedical datasets of different characteristics and sizes. Our findings reveal the effectiveness in terms of precision, recall and F-measure of the suggested heuristics and methods defining the framework to adapt mappings affected by KOS evolution. The obtained results contribute and improve the quality of mappings over time. CONCLUSIONS: The proposed framework can adapt mappings largely automatically, facilitating thus the maintenance task. The implemented algorithms and tools support and minimize the work of users in charge of KOS mapping maintenance.

A concept ideation framework for medical device design.

Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems.

MorphoCol: An ontology-based knowledgebase for the characterisation of clinically significant bacterial colony morphologies.

BACKGROUND: One of the major concerns of the biomedical community is the increasing prevalence of antimicrobial resistant microorganisms. Recent findings show that the diversification of colony morphology may be indicative of the expression of virulence factors and increased resistance to antibiotic therapeutics. To transform these findings, and upcoming results, into a valuable clinical decision making tool, colony morphology characterisation should be standardised. Notably, it is important to establish the minimum experimental information necessary to contextualise the environment that originated the colony morphology, and describe the main morphological features associated unambiguously. RESULTS: This paper presents MorphoCol, a new ontology-based tool for the standardised, consistent and machine-interpretable description of the morphology of colonies formed by human pathogenic bacteria. The Colony Morphology Ontology (CMO) is the first controlled vocabulary addressing the specificities of the morphology of clinically significant bacteria, whereas the MorphoCol publicly Web-accessible knowledgebase is an end-user means to search and compare CMO annotated colony morphotypes. Its ultimate aim is to help correlate the morphological alterations manifested by colony-forming bacteria during infection with their response to the antimicrobial treatments administered. CONCLUSIONS: MorphoCol is the first tool to address bacterial colony morphotyping systematically and deliver a free of charge resource to the community. Hopefully, it may introduce interesting features of analysis on pathogenic behaviour and play a significant role in clinical decision making. DATABASE URL: http://morphocol.org.

Locus-specific mutation databases: pitfalls and good practice based on the p53 experience.

Between 50,000 and 60,000 mutations have been described in various genes that are associated with a wide variety of diseases. Reporting, storing and analysing these data is an important challenge as such data provide invaluable information for both clinical medicine and basic science. Locus-specific databases have been developed to exploit this huge volume of data. The p53 mutation database is a paradigm, as it constitutes the largest collection of somatic mutations (22,000). However, there are several biases in this database that can lead to serious erroneous interpretations. We describe several rules for mutation database management that could benefit the entire scientific community.

Optimal query-based relevance feedback in medical image retrieval using score fusion-based classification.

In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

A systematic approach for using DICOM structured reports in clinical processes: focus on breast cancer.

This paper describes a methodology for redesigning the clinical processes to manage diagnosis, follow-up, and response to treatment episodes of breast cancer. This methodology includes three fundamental elements: (1) identification of similar and contrasting cases that may be of clinical relevance based upon a target study, (2) codification of reports with standard medical terminologies, and (3) linking and indexing the structured reports obtained with different techniques in a common system. The combination of these elements should lead to improvements in the clinical management of breast cancer patients. The motivation for this work is the adaptation of the clinical processes for breast cancer created by the Valencian Community health authorities to the new techniques available for data processing. To achieve this adaptation, it was necessary to design nine Digital Imaging and Communications in Medicine (DICOM) structured report templates: six diagnosis templates and three summary templates that combine reports from clinical episodes. A prototype system is also described that links the lesion to the reports. Preliminary tests of the prototype have shown that the interoperability among the report templates allows correlating parameters from different reports. Further work is in progress to improve the methodology in order that it can be applied to clinical practice.

Daily management systems in medicine.

"Lean" (continuous improvement) organizations make use of daily management systems (DMS) that are designed so that problems can be quickly identified, front-line staff are empowered to fix the problems that they can, and problems that the front-line staff cannot fix are escalated and countermeasures created quickly. Key components of a DMS include leadership standard work, visual controls, and a daily accountability process, as well as discipline involving each of these three components. The author's organization recently had the opportunity to open a new, nonreplacement hospital, allowing the incorporation of continuous improvement principles into the hospital's design and operations. One high-priority task was the creation of a DMS, which was structured as a tiered "huddle" system. All of the front-line clinical areas, as well as all clinical and nonclinical ancillary support areas, conduct morning huddles. Problems identified at these huddles and needing escalation are then brought to a patient flow huddle and an integrated huddle. All of these huddles occur daily and have a standard format with three clearly defined components: metrics-goal review, daily readiness assessment, and problem accountability reporting. The huddles also provide a daily opportunity to see and converse with the people with whom one needs to discuss certain issues. The process of bringing people together for these huddles can contribute significantly to team formation, coordination of efforts, and development of a culture of trust.