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ImportanceIdentifying new prognostic features in colon cancer has the potential to refine histopathologic review and inform patient care. Although prognostic artificial intelligence systems have recently demonstrated significant risk stratification for several cancer types, studies have not yet shown that the machine learning–derived features associated with these prognostic artificial intelligence systems are both interpretable and usable by pathologists.ObjectiveTo evaluate whether pathologist scoring of a histopathologic feature previously identified by machine learning is associated with survival among patients with colon cancer.Design, Setting, and ParticipantsThis prognostic study used deidentified, archived colorectal cancer cases from January 2013 to December 2015 from the University of Milano-Bicocca. All available histologic slides from 258 consecutive colon adenocarcinoma cases were reviewed from December 2021 to February 2022 by 2 pathologists, who conducted semiquantitative scoring for tumor adipose feature (TAF), which was previously identified via a prognostic deep learning model developed with an independent colorectal cancer cohort.Main Outcomes and MeasuresPrognostic value of TAF for overall survival and disease-specific survival as measured by univariable and multivariable regression analyses. Interpathologist agreement in TAF scoring was also evaluated.ResultsA total of 258 colon adenocarcinoma histopathologic cases from 258 patients (138 men [53%]; median age, 67 years [IQR, 65-81 years]) with stage II (n = 119) or stage III (n = 139) cancer were included. Tumor adipose feature was identified in 120 cases (widespread in 63 cases, multifocal in 31, and unifocal in 26). For overall survival analysis after adjustment for tumor stage, TAF was independently prognostic in 2 ways: TAF as a binary feature (presence vs absence: hazard ratio [HR] for presence of TAF, 1.55 [95% CI, 1.07-2.25]; P = .02) and TAF as a semiquantitative categorical feature (HR for widespread TAF, 1.87 [95% CI, 1.23-2.85]; P = .004). Interpathologist agreement for widespread TAF vs lower categories (absent, unifocal, or multifocal) was 90%, corresponding to a κ metric at this threshold of 0.69 (95% CI, 0.58-0.80).Conclusions and RelevanceIn this prognostic study, pathologists were able to learn and reproducibly score for TAF, providing significant risk stratification on this independent data set. Although additional work is warranted to understand the biological significance of this feature and to establish broadly reproducible TAF scoring, this work represents the first validation to date of human expert learning from machine learning in pathology. Specifically, this validation demonstrates that a computationally identified histologic feature can represent a human-identifiable, prognostic feature with the potential for integration into pathology practice.

Various biological resources, such as biobanks and disease-specific registries, have become indispensable resources to better understand the epidemiology and biological mechanisms of disease and are fundamental for advancing medical research. Nevertheless, biobanks and similar resources still face significant challenges to become more findable and accessible by users on both national and global scales. One of the main challenges for users is to find relevant resources using cataloging and search services such as the BBMRI-ERIC Directory, operated by European Research Infrastructure on Biobanking and Biomolecular Resources (BBMRI-ERIC), as these often do not contain the information needed by the researchers to decide if the resource has relevant material/data; these resources are only weakly characterized. Hence, the researcher is typically left with too many resources to explore and investigate. In addition, resources often have complex procedures for accessing holdings, particularly for depletable biological materials. This article focuses on designing a system for effective negotiation of access to holdings, in which a researcher can approach many resources simultaneously, while giving each resource team the ability to implement their own mechanisms to check if the material/data are available and to decide if access should be provided. The BBMRI-ERIC has developed and implemented an access and negotiation tool called the BBMRI-ERIC Negotiator. The Negotiator enables access negotiation to more than 600 biobanks from the BBMRI-ERIC Directory and other discovery services such as GBA/BBMRI-ERIC Locator or RD-Connect Finder. This article summarizes the principles that guided the design of the tool, the terminology used and underlying data model, request workflows, authentication and authorization mechanism(s), and the mechanisms and monitoring processes to stimulate the desired behavior of the resources: to effectively deliver access to biological material and data.

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Focal nodular hyperplasia (FNH) is a polyclonal tumour-like hepatic lesion characterised by parenchymal nodules, connective tissue septa without interlobular bile ducts, pronounced ductular reaction and inflammation. It may represent a response to local arterial hyperperfusion and hyperoxygenation resulting in oxidative stress. We aimed at obtaining closer insight into the pathogenesis of FNH with its characteristic morphologic features. Immunohistochemistry and immunofluorescence microscopy was performed on FNH specimens using antibodies against keratins (K) 7 and 19, neural cell adhesion molecule (NCAM), lamin B1, senescence markers (CDK inhibitor 1/p21Cip1, CDK inhibitor /p16Ink4a, senescence-associated (SA) β- galactosidase activity), proliferation markers (Ki-67, proliferating-cell nuclear antigen (PCNA)), and the abnormally phosphorylated histone γ-H2AX, indicating DNA double strand breaks; moreover SA β- galactosidase activity was determined histochemically. Ductular metaplasia of hepatocytes indicated by K7 expression in the absence of K19 plays a major role in the development of ductular reaction in FNH. Moreover, the expression of senescence markers (p21Cip1, p16Ink4a, γ-H2AX, SA β-galactosidase activity) in hepatocytes and cholangiocytes suggests that stress-induced cellular senescence contributes to fibrosis and inflammation via production of components of the senescence-associated secretory phenotype. Expression of proliferation markers (Ki-67, PCNA) was not enhanced in hepatocytes and biliary cells. Senescence and ductular metaplasia of hepatocytes may thus be involved in inflammation, fibrosis and apoptosis resistance. Hence, fibrosis, inflammation and reduced apoptotic cell death, rather than proliferation (hyperplasia) may be responsible for increased tissue mass and tumour-like appearance of FNH.

Gleason grading of prostate cancer is an important prognostic factor, but suffers from poor reproducibility, particularly among non-subspecialist pathologists. Although artificial intelligence (A.I.) tools have demonstrated Gleason grading on-par with expert pathologists, it remains an open question whether and to what extent A.I. grading translates to better prognostication. In this study, we developed a system to predict prostate cancer-specific mortality via A.I.-based Gleason grading and subsequently evaluated its ability to risk-stratify patients on an independent retrospective cohort of 2807 prostatectomy cases from a single European center with 5–25 years of follow-up (median: 13, interquartile range 9–17). Here, we show that the A.I.’s risk scores produced a C-index of 0.84 (95% CI 0.80–0.87) for prostate cancer-specific mortality. Upon discretizing these risk scores into risk groups analogous to pathologist Grade Groups (GG), the A.I. has a C-index of 0.82 (95% CI 0.78–0.85). On the subset of cases with a GG provided in the original pathology report (n = 1517), the A.I.’s C-indices are 0.87 and 0.85 for continuous and discrete grading, respectively, compared to 0.79 (95% CI 0.71–0.86) for GG obtained from the reports. These represent improvements of 0.08 (95% CI 0.01–0.15) and 0.07 (95% CI 0.00–0.14), respectively. Our results suggest that A.I.-based Gleason grading can lead to effective risk stratification, and warrants further evaluation for improving disease management. Gleason grading is the process by which pathologists assess the morphology of prostate tumors. The assigned Grade Group tells us about the likely clinical course of people with prostate cancer and helps doctors to make decisions on treatment. The process is complex and subjective, with frequent disagreement amongst pathologists. In this study, we develop and evaluate an approach to Gleason grading based on artificial intelligence, rather than pathologists’ assessment, to predict risk of dying of prostate cancer. Looking back at tumors and data from 2,807 people diagnosed with prostate cancer, we find that our approach is better at predicting outcomes compared to grading by pathologists alone. These findings suggest that artificial intelligence might help doctors to accurately determine the probable clinical course of people with prostate cancer, which, in turn, will guide treatment. Wulczyn et al. utilise a deep learning-based Gleason grading model to predict prostate cancer-specific mortality in a retrospective cohort of radical prostatectomy patients. Their model enables improved risk stratification compared to pathologists’ grading and demonstrates the potential for computational pathology in the management of prostate cancer.

Biliary tract cancer (BTC) represents a malignant tumor of the biliary tract including cholangiocarcinoma (CCA) and the carcinoma of the gallbladder (GBC) with a 5-year survival rate between 5 and 18% due to late diagnosis and rapid disease progression. Chronic inflammation is one of the main risk factors for CCA and GBC in particular. IL-6, as a mediator of inflammation, can act through a membrane-bound receptor alpha-chain (mIL-6R, "IL-6 classic signaling") or via soluble forms (sIL-6R, "IL-6 trans-signaling"). However, little is known about the impact on cellular responses of IL-6 trans-signaling on BTC. We analyzed primary tumors as whole sections and as tissue microarrays, and also searched The Cancer Genome Atlas database. Compared to non-neoplastic, non-inflamed gallbladder tissue, IL-6Rα was downregulated in GBC, and this correlated with the patients' overall survival. Furthermore, different CCA cell lines and compounds for activation (IL-6 and Hyper-IL-6) or inhibition (Tocilizumab and sgp130Fc) of IL-6 classic signaling and trans-signaling were used to determine their effects on cellular processes between the two modes of IL-6 signaling. Inhibition of IL-6 trans-signaling by sgp130Fc reduced CCA cell line viability and apoptosis, whereas migration and proliferation were increased. We conclude that IL-6Rα expression is a good prognostic marker for GBC, and that the blocking of IL-6 trans-signaling and activation of IL-6 classic signaling have tumor promoting activity. These findings warrant the exclusion of patients with GBC or other malignancies associated with bile metabolism from IL-6R inhibitor therapy.

Extracting hierarchical properties from Whole Slide Images automatically and expanding the possibilities of visualising data from digital pathology will not only drastically improve speed and accuracy of the pathologists’ work but also simplify the necessary pre-processing steps for machine learning tool-chains. The introduced pipeline identifies and converts areas of interest into binary masks and finds groups of areas that share similar locations using k-Means. This grouping is evaluated by the Silhouette Score which serves as a measure of confidence for the separability of clusters. Found objects are compared using structural similarities and HU-Moments. These results are then stored as measures of similarities creating virtual groups of the most similar objects. Finally, the information on similarities combined with further structural parameters are visualised.

Training in the medical field depends on the teaching of theoretical and practical skills. The acquisition of theoretical basics can be usually achieved by studying various documents, while the training of practical skills requires more intensive discussion and a special setup, such as an appropriate laboratory environment or a mentor-mentee connection. Aside from the conditions for transferring and gaining practical skills, teaching methods mostly concentrate on the sharing of explicit knowledge that can be designated and transferred easily.Therefore, the presented work focuses on the tracking of how to approach the diagnosis in pathological examinations and the visualization of any interaction with the specimen by recording microscopical exploration of an experienced pathologist. Those interactions include the full navigation path through a specimen, the panning and magnification of areas of interest, the observation duration as well as the spoken comments. Each maneuver and annotation that is relevant for approaching the diagnosis is documented, processed and visually prepared for trainees/residents to be able to comprehend argumentation via the look through the eyes of an expert.The collected data serve as a base for various medical applications like learning management systems to support education in the medical field, as well as machine learning and artificial intelligence applications in the area of digital pathology.

Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66–0.73) and 0.69 (95% CI: 0.64–0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R2 = 18% in both validation sets). Next, we generated human-interpretable histologic features by clustering embeddings from a deep-learning-based image-similarity model and showed that they explained the majority of the variance (R2 of 73–80%). Furthermore, the clustering-derived feature most strongly associated with high DLS scores was also highly prognostic in isolation. With a distinct visual appearance (poorly differentiated tumor cell clusters adjacent to adipose tissue), this feature was identified by annotators with 87.0–95.5% accuracy. Our approach can be used to explain predictions from a prognostic deep learning model and uncover potentially-novel prognostic features that can be reliably identified by people for future validation studies.

In this paper an automatic classification system for pathological findings is presented. The starting point in our undertaking was a pathologic tissue collection with about 1.4 million tissue samples described by free text records over 23 years. Exploring knowledge out of this “big data” pool is a challenging task, especially when dealing with unstructured data spanning over many years. The classification is based on an ontology-based term extraction and decision tree build with a manually curated classification system. The information extracting system is based on regular expressions and a text substitution system. We describe the generation of the decision trees by medical experts using a visual editor. Also the evaluation of the classification process with a reference data set is described. We achieved an F-Score of 89,7% for ICD-10 and an F-Score of 94,7% for ICD-O classification. For the information extraction of the tumor staging and receptors we achieved am F-Score ranging from 81,8 to 96,8%.

// Sonja M. Kessler 1, 2, * , Eva Lederer 1, * , Stephan Laggai 2 , Nicole Golob-Schwarzl 1, 3 , Kevan Hosseini 2 , Johannes Petzold 1 , Caroline Schweiger 1 , Robert Reihs 1 , Marlen Keil 4 , Jens Hoffmann 4 , Christian Mayr 5 , Tobias Kiesslich 5 , Martin Pichler 6, 7, 8 , Kyung Sik Kim 9 , Hyungjin Rhee 10 , Young Nyun Park 11 , Sigurd Lax 12 , Peter Obrist 13 , Alexandra K. Kiemer 2 and Johannes Haybaeck 1, 14 1 Institute of Pathology, Medical University of Graz, Graz, Austria 2 Department of Pharmacy, Pharmaceutical Biology, Saarland University, Saarbrucken, Germany 3 Center for Biomarker Research in Medicine, Graz, Austria 4 Experimental Pharmacology and Oncology Berlin-Buch GmbH, Berlin, Germany 5 Laboratory for Tumor Biology and Experimental Therapies, Institute of Physiology and Pathophysiology, Department of Internal Medicine I, Paracelsus Medical University, Salzburg, Austria 6 Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria 7 Research Unit for Non-coding RNAs and Genome Editing in Cancer, Medical University of Graz, Graz, Austria 8 Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA 9 Department of Surgery, Yonsei University College of Medicine, Seoul, South Korea 10 Department of Radiology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South Korea 11 Department of Pathology, Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, Seoul, South Korea 12 Department of Pathology, General Hospital Graz Sued-West, Graz, Austria 13 Laboratory of Pathology, Dr. Obrist and Dr. Brunhuber OG, Zams, Austria 14 Department of Pathology, Medical Faculty, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany * These authors contributed equally to this work Correspondence to: Johannes Haybaeck, email: johannes.haybaeck@med.ovgu.de Keywords: IGF2BP2/IMP2/p62, IGF2BP3/IMP3, IGF2BP1/IMP1, NADPH oxidase, gallbladder carcinoma Received: January 04, 2017 Accepted: September 04, 2017 Published: September 21, 2017 ABSTRACT Overexpression of the oncofetal insulin-like growth factor 2 mRNA-binding protein 2 (IMP2/IGF2BP2) has been described in different cancer types. Gallbladder carcinoma (GBC) is a rare but highly aggressive cancer entity with late clinical detection and poor prognosis. The aim of this study was to investigate the role of IMP2 in human GBC. Tissue microarrays (TMAs) of an international multi-center GBC sample collection from n = 483 patients were analyzed by immunohistochemistry. IMP2 immunoreactivity was found in 74.3% of the tumor samples on TMA, of which 14.0% showed strong and 86.0% low staining intensity. 72.4% of the tumor samples were IMP1 positive, but IMP1 showed lower expression in tumor tissue compared to control tissues. IMP3 immunoreactivity was observed in 92.7% of all tumors, of which 53.6% revealed strong IMP3 expression. Kaplan-Meier analysis linked high IMP2 expression to shorter survival time ( p = 0.033), whereas neither IMP1 nor IMP3 expression was linked to a decreased survival time. Eight different human biliary tract cancer (BTC) cell lines were evaluated for tumor growth kinetics in mouse xenografts. Cell lines with high IMP2 expression levels showed the fastest increase in tumor volumes in murine xenografts. Furthermore, IMP2 expression in these cells correlated with the generation of reactive oxygen species (ROS) and RAC1 expression in BTC cells, suggesting RAC1-induced ROS generation as a potential mechanism of IMP2-promoted progression of GBC. In conclusion, IMP2 is frequently overexpressed in GBC and significantly associated with poor prognosis and growth rates in vivo . IMP2 might therefore represent a new target for the treatment of advanced GBC.

Since pathology is supported by information technology new opportunities and questions have arisen. The digital age enables analyzing histopathological data with artificial intelligence methods to reveal further information and correlations. In this paper existing approaches to visualization of medical decision processes are presented as well as the relevance of explainability in decision making. The first step for implementing decision-paths in systems is to retrace an experienced pathologist’s diagnosis finding process. Recording a route through a landscape composed of human tissue in terms of a roadbook is one possible approach to collect information on how diagnoses are found. Choosing the roadbook metaphor provides a simple schema, that holds basic directions enriched with metadata regarding landmarks on a rally - in the context of pathology such landmarks provide information on the decision finding process.

We investigated the acceptance of virtual health avatars in geriatrics and gerontology, prevention and rehabilitation. We produced simulation videos for several scenarios and evaluated the acceptance with focus groups. The following research objectives were addressed: 1) the assessment of the acceptance of virtual health avatars 2) naming of the reasons for approval and rejection of the application of digital health technologies using the example of health avatars.The results obtained on the basis of the focus group discussions reveal a number of positive perspectives. The research results allow the identification of concerns towards the implementation of virtual health avatars and give hints how to improve interfaces for future data driven medicine.

The combination of ontologies with machine learning (ML) approaches is a hot topic and not yet extensively investigated but having great future potential. This is due to the general fact that both, ontologies and ML, constitute two indispensable technologies for domain-specific knowledge extraction, actively used in knowledge-based systems. Whilst the primary goal of both these approaches are the same, knowledge discovery, little is yet known about how the two sources of knowledge can be successfully integrated. The main data source in digital pathology are whole slide images. For the effective generation of sufficiently large and high-quality training data we need to extract in addition information from medical reports, containing non-standardized text. Since full annotation on pixel level would be impracticably expensive, a practical solution is in weakly-supervised ML. In the project described in this paper we used ontology-guided natural language processing (NLP) for term extraction and a decision tree built with an expert-curated classification system. This demonstrates the practical value of our solution to analyze and structure training data sets for ML and as a tool for the generation of biobank catalogues.

Advancements in Artificial Intelligence (AI) and Machine Learning (ML) are enabling new diagnostic capabilities. In this paper we argue that the very first step before introducing AI/ML into diagnostic workflows is a deep understanding of how pathologists work. To contribute to a deeper understanding of the diagnostic process in histopathology, we developed a visualization concept, including: (a) the sequence of the views observed by the pathologist (Observation Path), (b) the sequence of the spoken comments and statements of the pathologist (Dictation Path), (c) the underlying knowledge and experience of the pathologist (Knowledge Path), (d) information about the current phase of the diagnostic process and (e) the current magnification factor of the microscope chosen by the pathologist. We implemented the proofof-concept prototype as HTML5/CSS3/JavaScript application.

Accurate prognosis in colorectal cancer can have important implications for clinical management. Here, we develop a deep learning system (DLS) to first identify invasive cancer and then directly predict disease specific survival (DSS) for stage II and stage III colorectal cancer using only digitized histopathology whole-slide images. The DLS was trained using slides from 1173 stage II and 1266 stage III cases (18,304 total slides) and was evaluated on a held-out test set of 601 stage II and 638 stage III cases (9,340 total slides). The area under the receiver operating characteristic curve (AUC) for 5-year DSS prediction was 68.0 for stage II (95% CI 62.2-73.1) and 65.5 for stage III (95% CI 61.1-70.0). For stage II, 5-year DSS was 64% for DLS-predicted high-risk cases versus 89% for DLS-predicted low-risk cases (upper and lower risk quartiles; p Citation Format: Ellery Wulczyn, David F. Steiner, Melissa Moran, Markus Plass, Robert Reihs, Heimo Mueller, Apaar Sadhwani, Yuannan Cai, Isabelle Flament, Po-Hsuan Cameron Chen, Yun Liu, Martin C. Stumpe, Zhaoyang Xu, Kurt Zatloukal, Craig H. Mermel. A deep learning system to predict disease-specific survival in stage II and stage III colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2096.

In rare disease (RD) research, there is a huge need to systematically collect biomaterials, phenotypic, and genomic data in a standardized way and to make them findable, accessible, interoperable and reusable (FAIR). RD-Connect is a 6 years global infrastructure project initiated in November 2012 that links genomic data with patient registries, biobanks, and clinical bioinformatics tools to create a central research resource for RDs. Here, we present RD-Connect Registry & Biobank Finder, a tool that helps RD researchers to find RD biobanks and registries and provide information on the availability and accessibility of content in each database. The finder concentrates information that is currently sparse on different repositories (inventories, websites, scientific journals, technical reports, etc.), including aggregated data and metadata from participating databases. Aggregated data provided by the finder, if appropriately checked, can be used by researchers who are trying to estimate the prevalence of a RD, to organize a clinical trial on a RD, or to estimate the volume of patients seen by different clinical centers. The finder is also a portal to other RD-Connect tools, providing a link to the RD-Connect Sample Catalogue, a large inventory of RD biological samples available in participating biobanks for RD research. There are several kinds of users and potential uses for the RD-Connect Registry & Biobank Finder, including researchers collaborating with academia and the industry, dealing with the questions of basic, translational, and/or clinical research. As of November 2017, the finder is populated with aggregated data for 222 registries and 21 biobanks. This work has been supported by the European Union Seventh Framework Programme (FP7/20072013) under grant agreements no. 305444 (RD-Connect). RD-Connect has a main role in funding authors contributing to the study design, data collection and analysis, decision to publish, or preparation of the manuscrip. NeurOmics (no. 305121, http://rd-neuromics.eu/) and EURenOmics (no. 305608, http://www.eurenomics.eu/) have had mainly a role of data providers, since several registries participating to the Registry & Biobank Finder collaborate with the two projects. We thank ODEX4all (NWO 650.002.002), ELIXIR funded through participating member states, and ELIXIR-EXCELERATE funded through the European Commission within the Research Infrastructures Programme of Horizon 2020 (grant agreement number 676559) for collaborating to the study design and software development especially with a view to interoperability with other systems (MR, MT). MF and MM are funded by Fondazione Telethon (Project GTB12001; Telethon Network of Genetic Biobanks). Sí

Sample collections and data are hosted within different biobanks at diverse institutions across Europe. Our data integration framework aims at incorporating data about sample collections from different biobanks into a common research infrastructure, facilitating researchers' abilities to obtain high-quality samples to conduct their research. The resulting information must be locally gathered and distributed to searchable higher level information biobank directories to maximize the visibility on the national and European levels. Therefore, biobanks and sample collections must be clearly described and unambiguously identified. We describe how to tackle the challenges of integrating biobank-related data between biobank directories using heterogeneous data schemas and different technical environments.To establish a data exchange infrastructure between all biobank directories involved, we propose the following steps: (A) identification of core entities, terminology, and semantic relationships, (B) harmonization of heterogeneous data schemas of different Biobanking and Biomolecular Resources Research Infrastructure (BBMRI) directories, and (C) formulation of technical core principles for biobank data exchange between directories.(A) We identified the major core elements to describe biobanks in biobank directories. Since all directory data models were partially based on Minimum Information About BIobank Data Sharing (MIABIS) 2.0, the MIABIS 2.0 core model was used for compatibility. (B) Different projection scenarios were elaborated in collaboration with all BBMRI.at partners. A minimum set of mandatory and optional core entities and data items was defined for mapping across all directory levels. (C) Major core data exchange principles were formulated and data interfaces implemented by all biobank directories involved.We agreed on a MIABIS 2.0-based core set of harmonized biobank attributes and established a list of data exchange core principles for integrating biobank directories on different levels. This generic approach and the data exchange core principles proposed herein can also be applied in related tasks like integration and harmonization of biobank data on the individual sample and patient levels.

During the last decade pathology has benefited from the rapid progress of image digitizing technologies, which led to the development of scanners, capable to produce so-called Whole Slide images (WSI) which can be explored by a pathologist on a computer screen comparable to the conventional microscope and can be used for diagnostics, research, archiving and also education and training. Digital pathology is not just the transformation of the classical microscopic analysis of histological slides by pathologists to just a digital visualization. It is a disruptive innovation that will dramatically change medical work-flows in the coming years and help to foster personalized medicine. Really powerful gets a pathologist if she/he is augmented by machine learning, e.g. by support vector machines, random forests and deep learning. The ultimate benefit of digital pathology is to enable to learn, to extract knowledge and to make predictions from a combination of heterogenous data, i.e. the histological image, the patient history and the *omics data. These challenges call for integrated/integrative machine learning approach fostering transparency, trust, acceptance and the ability to explain step-by-step why a decision has been made.

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Today the translation of NGS technology to (cancer) diagnostics and therapeutics is widely anticipated. In this paper we give an overview of design, user experience and usability (DUXU) requirements for NGS applications with a special focus on clinical and diagnostic settings and describe a data driven GUI and visualization framework for a NGS based clinical decision support system (DSS). In such a DSS it is hard to oversee the huge amount of information, especially if given only in textual form. To overcome this we developed a visual language for electronic health records (EHR) for navigation within a case and comparison of medical cases.

Next-generation sequencing (NGS) has transformed genomic research by decreasing the cost of sequencing and increasing the throughput. Today, translation of NGS technology to (cancer) diagnostics and therapeutics is widely anticipated. In this paper we give an overview of design, user experience and usability (DUXU) requirements for NGS applications with a special focus on clinical and diagnostic settings.

Biobanks are essential for the realization of P4-medicine, hence indispensable for smart health. One of the grand challenges in biobank research is to close the research cycle in such a way that all the data generated by one research study can be consistently associated to the original samples, therefore data and knowledge can be reused in other studies. A catalogue must provide the information hub connecting all relevant information sources. The key knowledge embedded in a biobank catalogue is the availability and quality of proper samples to perform a research project. Depending on the study type, the samples can reflect a healthy reference population, a cross sectional representation of a certain group of people (healthy or with various diseases) or a certain disease type or stage. To overview and compare collections from different catalogues, we introduce visual analytics techniques, especially glyph based visualization techniques, which were successfully applied for knowledge discovery of single biobank catalogues. In this paper, we describe the state-of-the art in the integration of biobank catalogues addressing the challenge of combining heterogeneous data sources in a unified and meaningful way, consequently enabling the discovery and visualization of data from different sources. Finally we present open questions both in data integration and visualization of unified catalogues and propose future research in data integration with a linked data approach and the fusion of multi level glyph and network visualization.

As a hub of information controlled by the patient, personal health records (PHR) collect information from the patient medical history including a wide variety of data sources as patient's observations, lab results, clinical findings and in the future maybe even personal genetic data and automatic recordings from monitoring devices. This development will on the one hand make health care more personalized and user controlled but on the other hand also overloads consumers with a huge amount of data. To address this issue we developed a framework for adaptive visual symbols (AVS). An AVS can adapt its appearance and level of detail during the communication process. Finally we demonstrate the AVS principle for the visualization of personal health records.

The domain of biobanking has gone through many stages and as a result there are a wide range of commercial and open source software solutions available. The utilization of these software tools requires different levels of domain and technical skills for installation, configuration and ultimate us of these biobank software tools. To compound this complexity the biobanking community are required to work together in order to share knowledge and jointly build solutions to underpin the research infrastructure. We have evaluated the available tools, described them in a catalogue (BiobankApps) and made a selection of tools available to biobanks in a reference toolbox (BIBBOX) that are use-case driven. In the BiobankApps tool catalogue, both commercial and open source software solutions related to the biobanking domain are included, classified and evaluated. The evaluation covers: 1) "user review" by an authenticated user 2) domain expert: quick analysis by BBMRI members and 3) domain expert: detailed analysis and test installation with real world data. The evaluation is paired with a survey across the more "advanced" (from a technology perspective) biobanks to investigate what tools are currently used and summarises known benefits/drawbacks of the respective packages. In the second step we recommend tools for specific use cases, and install, configure and connect these in the BIBBOX framework. This service also builds on the existing work in the United Kingdom in seeking to establish the motivations for different stakeholders to become involved and therefore assisting in prioritising the use-cases based on the level of need and support within the research community. All tools associated to a use-case are available as BIBBOX applications (technically this is achieved by docker containers), which are integrated in the BIBBOX framework with central identification and user management. In future work we plan to share the acquired knowledge with other networks, develop an Application Programmable Interface (API) for the exchange of metadata with other tool catalogues and work on an ontology for the evaluation of biobank software.

Background This paper presents multilevel data glyphs optimized for the interactive knowledge discovery and visualization of large biomedical data sets. Data glyphs are three- dimensional objects defined by multiple levels of geometric descriptions (levels of detail) combined with a mapping of data attributes to graphical elements and methods, which specify their spatial position. Methods In the data mapping phase, which is done by a biomedical expert, meta information about the data attributes (scale, number of distinct values) are compared with the visual capabilities of the graphical elements in order to give a feedback to the user about the correctness of the variable mapping. The spatial arrangement of glyphs is done in a dimetric view, which leads to high data density, a simplified 3D navigation and avoids perspective distortion. Results We show the usage of data glyphs in the disease analyser a visual analytics application for personalized medicine and provide an outlook to a biomedical web visualization scenario. Conclusions Data glyphs can be successfully applied in the disease analyser for the analysis of big medical data sets. Especially the automatic validation of the data mapping, selection of subgroups within histograms and the visual comparison of the value distributions were seen by experts as an important functionality.