Radiomics: A primer for the radiation oncologist.

PURPOSE: Radiomics are a set of methods used to leverage medical imaging and extract quantitative features that can characterize a patient's phenotype. All modalities can be used with several different software packages. Specific informatics methods can then be used to create meaningful predictive models. In this review, we will explain the major steps of a radiomics analysis pipeline and then present the studies published in the context of radiation therapy. METHODS: A literature review was performed on Medline using the search engine PubMed. The search strategy included the search terms "radiotherapy", "radiation oncology" and "radiomics". The search was conducted in July 2019 and reference lists of selected articles were hand searched for relevance to this review. RESULTS: A typical radiomics workflow always includes five steps: imaging and segmenting, data curation and preparation, feature extraction, exploration and selection and finally modeling. In radiation oncology, radiomics studies have been published to explore different clinical outcome in lung (n=5), head and neck (n=5), esophageal (n=3), rectal (n=3), pancreatic (n=2) cancer and brain metastases (n=2). The quality of these retrospective studies is heterogeneous and their results have not been translated to the clinic. CONCLUSION: Radiomics has a great potential to predict clinical outcome and better personalize treatment. But the field is still young and constantly evolving. Improvement in bias reduction techniques and multicenter studies will hopefully allow more robust and generalizable models.

[Basis and perspectives of artificial intelligence in radiation therapy]. / Intelligence artificielle et radiothérapie : quelles bases et quelles perspectives ?

Artificial intelligence is a highly polysemic term. In computer science, with the objective of being able to solve totally new problems in new contexts, artificial intelligence includes connectionism (neural networks) for learning and logics for reasoning. Artificial intelligence algorithms mimic tasks normally requiring human intelligence, like deduction, induction, and abduction. All apply to radiation oncology. Combined with radiomics, neural networks have obtained good results in image classification, natural language processing, phenotyping based on electronic health records, and adaptive radiation therapy. General adversial networks have been tested to generate synthetic data. Logics based systems have been developed for providing formal domain ontologies, supporting clinical decision and checking consistency of the systems. Artificial intelligence must integrate both deep learning and logic approaches to perform complex tasks and go beyond the so-called narrow artificial intelligence that is tailored to perform some highly specialized task. Combined together with mechanistic models, artificial intelligence has the potential to provide new tools such as digital twins for precision oncology.

[Clinical photography by smartphone in plastic surgery and protection of personal data: Development of a secured platform and application on 979 patients]. / Photographie clinique par smartphone en chirurgie plastique et protection des données personnelles : développement d'une plateforme sécurisée et application sur 979 patients.

BACKGROUND: The clinical photography in plastic and reconstructive surgery has known a numerical breakthrough. The storage of online data, massive means of analysis such as facial recognitions algorithms poses a serious issue when it comes to the protection of personal data. We will assess a platform's benefits in connection with the computerized medical record, which will allow keeping the photos filed and centralized in a smart and secure manner. METHOD: We interviewed 300 plastic surgeons about the role of smartphone in their clinical practice. Concomitantly, we developed an innovative platform called Surgeon©, a secure way to index, file and send photographs with a smartphone on our hospital's server. Each photographic sequence was qualified using a specific form. We then collected prospectively, between May 1st 2017 and March 30th 2018, the number of patients photographed, the number of sequences and photographs taken and the average number of sequences per patient. RESULTS: Out of 86 French plastic surgeons surveyed, 81% say that they could not go on with their daily practice today without their smartphone. Photographs taken were stored in their smartphones (50%) or synced with virtual storage (25.6%). A majority (80.2%) would use a dedicated secured smartphone application. Our application allowed us to photograph 979 patients, or 2345 sequences and 8112 photographs, with an average of 2.28 sequences per patient. CONCLUSION: Thanks to its ergonomics and security, this platform can be set up in a hospital ward and beyond.

Health Data for Public Health: Towards New Ways of Combining Data Sources to Support Research Efforts in Europe.

Objectives: To present the European landscape regarding the re-use of health administrative data for research. Methods: We present some collaborative projects and solutions that have been developed by Nordic countries, Italy, Spain, France, Germany, and the UK, to facilitate access to their health data for research purposes. Results: Research in public health is transitioning from siloed systems to more accessible and re-usable data resources. Following the example of the Nordic countries, several European countries aim at facilitating the re-use of their health administrative databases for research purposes. However, the ecosystem is still a complex patchwork, with different rules, policies, and processes for data provision. Conclusion: The challenges are such that with the abundance of health administrative data, only a European, overarching public health research infrastructure, is able to efficiently facilitate access to this data and accelerate research based on these highly valuable resources.

An architecture for genomics analysis in a clinical setting using Galaxy and Docker.

Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment. Clinical bioinformatics handles the manipulation of the data generated by the sequencer, from the generation to the analysis and interpretation. Reproducibility and traceability are crucial issues in a clinical setting. We have designed an approach based on Docker container technology and Galaxy, the popular bioinformatics analysis support open-source software. Our solution simplifies the deployment of a small-size analytical platform and simplifies the process for the clinician. From the technical point of view, the tools embedded in the platform are isolated and versioned through Docker images. Along the Galaxy platform, we also introduce the AnalysisManager, a solution that allows single-click analysis for biologists and leverages standardized bioinformatics application programming interfaces. We added a Shiny/R interactive environment to ease the visualization of the outputs. The platform relies on containers and ensures the data traceability by recording analytical actions and by associating inputs and outputs of the tools to EDAM ontology through ReGaTe. The source code is freely available on Github at https://github.com/CARPEM/GalaxyDocker.

[Artificial intelligence applied to radiation oncology]. / Intelligence artificielle appliquée à la radiothérapie.

Performing randomised comparative clinical trials in radiation oncology remains a challenge when new treatment modalities become available. One of the most recent examples is the lack of phase III trials demonstrating the superiority of intensity-modulated radiation therapy in most of its current indications. A new paradigm is developing that consists in the mining of large databases to answer clinical or translational issues. Beyond national databases (such as SEER or NCDB), that often lack the necessary level of details on the population studied or the treatments performed, electronic health records can be used to create detailed phenotypic profiles of any patients. In parallel, the Record-and-Verify Systems used in radiation oncology precisely document the planned and performed treatments. Artificial Intelligence and machine learning algorithms can be used to incrementally analyse these data in order to generate hypothesis to better personalize treatments. This review discusses how these methods have already been used in previous studies.

Clinical data integration model. Core interoperability ontology for research using primary care data.

INTRODUCTION: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". BACKGROUND: Primary care data is the single richest source of routine health care data. However its use, both in research and clinical work, often requires data from multiple clinical sites, clinical trials databases and registries. Data integration and interoperability are therefore of utmost importance. OBJECTIVES: TRANSFoRm's general approach relies on a unified interoperability framework, described in a previous paper. We developed a core ontology for an interoperability framework based on data mediation. This article presents how such an ontology, the Clinical Data Integration Model (CDIM), can be designed to support, in conjunction with appropriate terminologies, biomedical data federation within TRANSFoRm, an EU FP7 project that aims to develop the digital infrastructure for a learning healthcare system in European Primary Care. METHODS: TRANSFoRm utilizes a unified structural / terminological interoperability framework, based on the local-as-view mediation paradigm. Such an approach mandates the global information model to describe the domain of interest independently of the data sources to be explored. Following a requirement analysis process, no ontology focusing on primary care research was identified and, thus we designed a realist ontology based on Basic Formal Ontology to support our framework in collaboration with various terminologies used in primary care. RESULTS: The resulting ontology has 549 classes and 82 object properties and is used to support data integration for TRANSFoRm's use cases. Concepts identified by researchers were successfully expressed in queries using CDIM and pertinent terminologies. As an example, we illustrate how, in TRANSFoRm, the Query Formulation Workbench can capture eligibility criteria in a computable representation, which is based on CDIM. CONCLUSION: A unified mediation approach to semantic interoperability provides a flexible and extensible framework for all types of interaction between health record systems and research systems. CDIM, as core ontology of such an approach, enables simplicity and consistency of design across the heterogeneous software landscape and can support the specific needs of EHR-driven phenotyping research using primary care data.

Automated classification of free-text pathology reports for registration of incident cases of cancer.

OBJECTIVE: Our study aimed to construct and evaluate functions called "classifiers", produced by supervised machine learning techniques, in order to categorize automatically pathology reports using solely their content. METHODS: Patients from the Poitou-Charentes Cancer Registry having at least one pathology report and a single non-metastatic invasive neoplasm were included. A descriptor weighting function accounting for the distribution of terms among targeted classes was developed and compared to classic methods based on inverse document frequencies. The classification was performed with support vector machine (SVM) and Naive Bayes classifiers. Two levels of granularity were tested for both the topographical and the morphological axes of the ICD-O3 code. The ability to correctly attribute a precise ICD-O3 code and the ability to attribute the broad category defined by the International Agency for Research on Cancer (IARC) for the multiple primary cancer registration rules were evaluated using F1-measures. RESULTS: 5121 pathology reports produced by 35 pathologists were selected. The best performance was achieved by our class-weighted descriptor, associated with a SVM classifier. Using this method, the pathology reports were properly classified in the IARC categories with F1-measures of 0.967 for both topography and morphology. The ICD-O3 code attribution had lower performance with a 0.715 F1-measure for topography and 0.854 for morphology. CONCLUSION: These results suggest that free-text pathology reports could be useful as a data source for automated systems in order to identify and notify new cases of cancer. Future work is needed to evaluate the improvement in performance obtained from the use of natural language processing, including the case of multiple tumor description and possible incorporation of other medical documents such as surgical reports.

Accessing and integrating data and knowledge for biomedical research.

OBJECTIVES: To review the issues that have arisen with the advent of translational research in terms of integration of data and knowledge, and survey current efforts to address these issues. METHODS: Using examples form the biomedical literature, we identified new trends in biomedical research and their impact on bioinformatics. We analyzed the requirements for effective knowledge repositories and studied issues in the integration of biomedical knowledge. RESULTS: New diagnostic and therapeutic approaches based on gene expression patterns have brought about new issues in the statistical analysis of data, and new workflows are needed are needed to support translational research. Interoperable data repositories based on standard annotations, infrastructures and services are needed to support the pooling and meta-analysis of data, as well as their comparison to earlier experiments. High-quality, integrated ontologies and knowledge bases serve as a source of prior knowledge used in combination with traditional data mining techniques and contribute to the development of more effective data analysis strategies. CONCLUSION: As biomedical research evolves from traditional clinical and biological investigations towards omics sciences and translational research, specific needs have emerged, including integrating data collected in research studies with patient clinical data, linking omics knowledge with medical knowledge, modeling the molecular basis of diseases, and developing tools that support in-depth analysis of research data. As such, translational research illustrates the need to bridge the gap between bioinformatics and medical informatics, and opens new avenues for biomedical informatics research.

[Vertebral trabecular network analysis on CT images]. / Analyse de la trame osseuse vertébrale sur coupes scanographiques.

PURPOSE: Comparing texture analysis, density measurement and visual quantification of trabecular network on spine CT images, to better evaluate bone architecture in osteoporosis. METHOD AND MATERIALS: Seventeen patients, aged 19 to 84 years, were included. One patient presented osteoporotic fractures. High resolution computed tomographic (HR-CT) images of the third lumbar vertebra were acquired using a Somatom 4 plus CT (Siemens) in a strict axial orientation with FOV of 12 cm and slice thickness of 1 mm. The size of the Region Of Interest was 1,6 cm(2). Three analyses were performed on this ROI: Density (in Hounsfield Unity), texture analysis (run length) and features inspired from bone histomorphometry (Bone Volume/Tissue Volume). RESULTS: Density measurement, run length methods and BV/TV provided consistent results with regards to age. Indeed density, run length and BV/TV results were lower for older patients with more advanced bone trabeculra alterations. CONCLUSION: Only BV/TV and run length parameters seemed to show additional information on trabecular network architecture. The contribution of these two measurements to diagnose and classify osteoporosis will be the goal of a clinical study.

Methods for exploring the semantics of the relationships between co-occurring UMLS concepts.

OBJECTIVE: To characterize the relationships among UMLS concepts that co-occur as MeSH descriptors in MEDLINE citations (1990-1999). DESIGN: 18,485 UMLS concepts involved in 7,928,608 directed pairs of co-occurring concepts were studied. For each directed pair of concepts C1-C2: (i) the "family" of C1 was built, using the UMLS Metathesaurus, and we tested whether or not C2 belonged to C1's family; (ii) we used the semantic categorization of Metathesaurus concepts through the UMLS Semantic Network and Semantic Groups to represent the semantics of the relationships between C1 and C2. RESULTS: In 6.5% of the directed pairs, the co-occurring concept C2 was found within the "family" of C1. Detailed results are given. The most frequent co-occurrences involved "Chemicals and Drugs" and "Chemicals and Drugs", as well as "Disorders" and "Chemicals and Drugs". DISCUSSION: This work takes advantage of both symbolic and statistical information represented in the UMLS, and analyzes their overlap. Further research is suggested.

Aggregating UMLS semantic types for reducing conceptual complexity.

The conceptual complexity of a domain can make it difficult for users of information systems to comprehend and interact with the knowledge embedded in those systems. The Unified Medical Language System (UMLS) currently integrates over 730,000 biomedical concepts from more than fifty biomedical vocabularies. The UMLS semantic network reduces the complexity of this construct by grouping concepts according to the semantic types that have been assigned to them. For certain purposes, however, an even smaller and coarser-grained set of semantic type groupings may be desirable. In this paper, we discuss our approach to creating such a set. We present six basic principles, and then apply those principles in aggregating the existing 134 semantic types into a set of 15 groupings. We present some of the difficulties we encountered and the consequences of the decisions we have made. We discuss some possible uses of the semantic groups, and we conclude with implications for future work.

Design of a CSCL environment for clinical reasoning learning and problem-based learning in medicine.

The medical curriculum is changing, student-centered learning is currently used in medical schools. Problem-based Learning and Clinical Reasoning Learning develop the students' reasoning strategies. CSCW (Computer-Supported Cooperative Work) technology is used in Problem-Based Learning systems. We have designed a CSCL (Computer-Supported Collaborative Learning) environment for improving group coordination and communication in Clinical Reasoning Learning sessions. To support these new educative technologies, a prototype has been developed.

Sharing medical data for patient path analysis with data mining method.

The Agora Data project started in October 1997 in France. The objective was to share medical data between several medical institutions to analysis medical care pathways for patients that suffer from low back pain. The analysis of the medical records decomposed in three steps allowed us to produce knowledge on medical contacts of patients with the health care system. In order to study the relations between these contacts, we created medical path of patients within the framework of the possible contacts we had isolated. This work relates the implementation and the first results of the pilot study.

Issues in the design of medical ontologies used for knowledge sharing.

Recent work in Medical Informatics is exploring the development and the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among several computer systems. We describe the role of ontologies in supporting knowledge sharing activities in medicine Principles for the design of ontologies have been proposed, mainly in other domains: these principles include parsimony, clarity, representation of categories versus terms, and coherence. We analyze how and why these principles can or cannot be applied from case studies from medical systems. Regarding the fact that most of medical concepts are empirical, selected design decisions are discussed. An alternative representation choice consists in mapping principled general core ontologies and domain ontologies.

Mapping the UMLS Semantic Network into general ontologies.

In this study, we analyzed the compatibility between an ontology of the biomedical domain (the UMLS Semantic Network) and two other ontologies: the Upper Cyc Ontology (UCO) and WordNet. 1) We manually mapped UMLS Semantic Types to UCO. One fifth of the UMLS Semantic Types had exact mapping to UCO types. UCO provides generic concepts and a structure that relies on a larger number of categories, despite its lack of depth in the biomedical domain. 2) We compared semantic classes in the UMLS and WordNet. 2% of the UMLS concepts from the Health Disorder class were present in WordNet, and compatibility between classes was 48%. WordNet, as a general language-oriented ontology is a source of lay knowledge, particularly important for consumer health applications.

Knowledge acquisition to qualify Unified Medical Language System interconceptual relationships.

Adding automatically relations between concepts from a database to a knowledge base such as the Unified Medical Language System can be very useful to increase the consistency of the latter one. But the transfer of qualified relationships is more interesting. The most important interest of these new acquisitions is that the UMLS became more compliant and medically pertinent to be used in different medical applications. This paper describes the possibility to inherit automatically medical inter-conceptual relationships qualifiers from a disease description included into a database and to integrate them into the UMLS knowledge base. The paper focuses on the transmission of knowledge from a French medical database to an English one.

The French Virtual Medical University.

This paper is the description of a French Virtual Medical University based on the federation of existing or currently being developed resources in several Medical Schools in France. The objectives of the project is not only to share experiences across the country but also to integrate several resources using the New Information and Communication Technologies to support new pedagogical approaches for medical students and also for continuing medical education. The project includes: A virtual Medical Campus using secure access from several sites, The Integration of new interactive resources based on pedagogical methods, Implementation of new indexing and search engines based on medical vocabularies and ontologies, The definition of general and specific portals, the evaluation of the system for ergonomics and contents.

A method for an automated decomposition of a complex term into its conceptual components.

The paper deals with the improvement of the MAOUSSC model (Modèle d'Aide et d'Orientation d'un Utilisateur au Sein des Systèmes de Codage) and system. Its specific purpose is the automation of the description of medical and surgical procedures. We have developed an automatic decomposition method using a linguistic and conceptual approach based on the UMLS knowledge base. This work concerns the processing of 100 procedure wordings from the digestive surgery domain. We introduce a prototype of such a system automating the decomposition through a web interface.