Mapping the Oncological Basis Dataset to the Standardized Vocabularies of a Common Data Model: A Feasibility Study.

In their joint effort against cancer, all involved parties within the German healthcare system are obligated to report diagnostics, treatments, progression, and follow-up information for tumor patients to the respective cancer registries. Given the federal structure of Germany, the oncological basis dataset (oBDS) operates as the legally required national standard for oncological reporting. Unfortunately, the usage of various documentation software solutions leads to semantic and technical heterogeneity of the data, complicating the establishment of research networks and collective data analysis. Within this feasibility study, we evaluated the transferability of all oBDS characteristics to the standardized vocabularies, a metadata repository of the observational medical outcomes partnership (OMOP) common data model (CDM). A total of 17,844 oBDS expressions were mapped automatically or manually to standardized concepts of the OMOP CDM. In a second step, we converted real patient data retrieved from the Hamburg Cancer Registry to the new terminologies. Given our pipeline, we transformed 1773.373 cancer-related data elements to the OMOP CDM. The mapping of the oBDS to the standardized vocabularies of the OMOP CDM promotes the semantic interoperability of oncological data in Germany. Moreover, it allows the participation in network studies of the observational health data sciences and informatics under the usage of federated analysis beyond the level of individual countries.

Surgical data science - from concepts toward clinical translation.

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research.

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Analysis of Not Structurable Oncological Study Eligibility Criteria for Improved Patient-Trial Matching.

BACKGROUND: Higher enrolment rates of cancer patients into clinical trials are necessary to increase cancer survival. As a prerequisite, an improved semiautomated matching of patient characteristics with clinical trial eligibility criteria is needed. This is based on the computer interpretability, i.e., structurability of eligibility criteria texts. To increase structurability, the common content, phrasing, and structuring problems of oncological eligibility criteria need to be better understood. OBJECTIVES: We aimed to identify oncological eligibility criteria that were not possible to be structured by our manual approach and categorize them by the underlying structuring problem. Our results shall contribute to improved criteria phrasing in the future as a prerequisite for increased structurability. METHODS: The inclusion and exclusion criteria of 159 oncological studies from the Clinical Trial Information System of the National Center for Tumor Diseases Heidelberg were manually structured and grouped into content-related subcategories. Criteria identified as not structurable were analyzed further and manually categorized by the underlying structuring problem. RESULTS: The structuring of criteria resulted in 4,742 smallest meaningful components (SMCs) distributed across seven main categories (Diagnosis, Therapy, Laboratory, Study, Findings, Demographics, and Lifestyle, Others). A proportion of 645 SMCs (13.60%) was not possible to be structured due to content- and structure-related issues. Of these, a subset of 415 SMCs (64.34%) was considered not remediable, as supplementary medical knowledge would have been needed or the linkage among the sentence components was too complex. The main category "Diagnosis and Study" contained these two subcategories to the largest parts and thus were the least structurable. In the inclusion criteria, reasons for lacking structurability varied, while missing supplementary medical knowledge was the largest factor within the exclusion criteria. CONCLUSION: Our results suggest that further improvement of eligibility criterion phrasing only marginally contributes to increased structurability. Instead, physician-based confirmation of the matching results and the exclusion of factors harming the patient or biasing the study is needed.

Modern Information Technology for Cancer Research: What's in IT for Me? An Overview of Technologies and Approaches.

Information technology (IT) can enhance or change many scenarios in cancer research for the better. In this paper, we introduce several examples, starting with clinical data reuse and collaboration including data sharing in research networks. Key challenges are semantic interoperability and data access (including data privacy). We deal with gathering and analyzing genomic information, where cloud computing, uncertainties and reproducibility challenge researchers. Also, new sources for additional phenotypical data are shown in patient-reported outcome and machine learning in imaging. Last, we focus on therapy assistance, introducing tools used in molecular tumor boards and techniques for computer-assisted surgery. We discuss the need for metadata to aggregate and analyze data sets reliably. We conclude with an outlook towards a learning health care system in oncology, which connects bench and bedside by employing modern IT solutions.

Finding Options Beyond Standard of Care in Oncology: A Proposal for Workflows Utilizing Knowledge Databases.

With the novel approach of molecularly stratified therapies based on genetic characteristics of individual tumors, the need for databases providing information on molecular alterations and targeted treatment options is increasing rapidly. In Molecular Tumor Boards (MTB) professionals discuss molecular alterations and provide biological context for therapeutic options using external knowledge databases. The identification of informative databases and the information on their specific contents can greatly facilitate and standardize the functioning of a MTB. In this work we present a list of databases which have been deemed useful and relevant for MTB in a clinical setting. We describe workflows to recommend the use of specific databases at different steps in the clinical curation process. Information obtained from these databases is a necessary prerequisite to evaluate molecular alterations and devise rational targeted therapies in MTB.

An Interactive Timeline Visualization for Patient Cohorts in the Oncological Routine: A Use Case on Multiple Myeloma.

With the growing interdisciplinarity of cancer treatment and increasing amounts of data and patients, it is getting increasingly difficult for physicians to capture a patient's medical history as a basis for adequate treatment and to compare different medical histories of similar patients to each other. Furthermore, in order to tackle the etiological mechanisms of cancer, it is crucial to identify patients exhibiting a different disease course than their corresponding cohort. Several timeline visualizations have already been proposed. However, the functions and design of such visualizations are always use case dependent. We constructed a cohort timeline prototype mock-up for a specific oncological use case involving multiple myeloma, where the chronological monitoring of various parameters is crucial for patient diagnosis and treatment. Our proposed cohort timeline is a synthesis between elements described in the literature and our own approaches regarding function and design.

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.

TeamTreat--a communication platform for concerted cancer treatment.

TeamTreat is an internet platform providing a case record for cancer patients across and inside the primary and secondary health care sector. Due to the slow progress of cross-institutional integration in healthcare IT, we created an alternative low-level approach to this problem and put special emphasis on an easy access for healthcare professionals regardless of the specific IT infrastructure they use. Physicians use the platform to share and collect information to achieve a collaborative treatment of cancer. Furthermore, the data in the case record is searchable for clinical researchers to find suitable patients for inclusion in clinical trials. Reading access for patients is also possible.

IT behind a platform for Translational Cancer Research - concept and objectives.

The German Consortium for Translational Cancer Research (DKTK) and the Rhine-Main Translational Cancer Research Network (RM-TCRN) are designed to exploit large population cohorts of cancer patients for the purpose of bio-banking, clinical trials, and clinical cancer registration. Hence, the success of these platforms is heavily dependent on the close interlinking of clinical data from cancer patients, information from study registries, and data from bio-banking systems of different laboratories and scientific institutions. This article referring to the poster discusses the main challenges of the platforms from an information technology point of view, legal and data security issues, and outlines an integrative IT-concept concerning a decentralized, distributed search approach where data management and search is in compliance with existing legislative rules.

Technical data evaluation of a palliative care web-based documentation system.

A technical analysis of the web-based patient documentation system, eKernPäP, was conducted. The system is used by interdisciplinary pediatric palliative care teams in Germany to document outpatient care. The data of the system and the data of an external web analytic system have been evaluated. The results gave an overview how the system is used and what information is generated. A detailed analysis of singular forms showed that not all forms were filled in completely. With the help of the external web analytic system the navigation behavior of the users could be retraced. The users followed the given navigation from top to bottom. An existing exception in this pattern turned out to be misplacement and will be corrected in the next version. The technical analysis proved to be a good tool for improving a web-based documentation system.

Giant cavernous haemangioma with Kasabach-Merritt syndrome: a case report and review.

UNLABELLED: We report the case of a 4-month-old boy presenting with a giant cutaneous haemangioma complicated by Kasabach-Merritt syndrome (KMS) with severe thrombocytopenia. After poor response to corticosteroid therapy and subsequent treatment with interferon alpha-2a, radiotherapy led to tumour regression and resolution of the disseminated intravascular coagulopathy over a 14-month period of follow up. Whereas the various available treatment options are reviewed and discussed in this article, the therapy of choice should be chosen individually. CONCLUSION: to date prospective randomised and controlled trials are required to investigate the optimal management of patients with Kasabach-Merritt syndrome.