Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Provenance of specimen and data - A prerequisite for AI development in computational pathology.

AI development in biotechnology relies on high-quality data to train and validate algorithms. The FAIR principles (Findable, Accessible, Interoperable, and Reusable) and regulatory frameworks such as the In Vitro Diagnostic Regulation (IVDR) and the Medical Device Regulation (MDR) specify requirements on specimen and data provenance to ensure the quality and traceability of data used in AI development. In this paper, a framework is presented for recording and publishing provenance information to meet these requirements. The framework is based on the use of standardized models and protocols, such as the W3C PROV model and the ISO 23494 series, to capture and record provenance information at various stages of the data generation and analysis process. The framework and use case illustrate the role of provenance information in supporting the development of high-quality AI algorithms in biotechnology. Finally, the principles of the framework are illustrated in a simple computational pathology use case, showing how specimen and data provenance can be used in the development and documentation of an AI algorithm. The use case demonstrates the importance of managing and integrating distributed provenance information and highlights the complex task of considering factors such as semantic interoperability, confidentiality, and the verification of authenticity and integrity.

Lightweight Distributed Provenance Model for Complex Real-world Environments.

Provenance is information describing the lineage of an object, such as a dataset or biological material. Since these objects can be passed between organizations, each organization can document only parts of the objects life cycle. As a result, interconnection of distributed provenance parts forms distributed provenance chains. Dependant on the actual provenance content, complete provenance chains can provide traceability and contribute to reproducibility and FAIRness of research objects. In this paper, we define a lightweight provenance model based on W3C PROV that enables generation of distributed provenance chains in complex, multi-organizational environments. The application of the model is demonstrated with a use case spanning several steps of a real-world research pipeline - starting with the acquisition of a specimen, its processing and storage, histological examination, and the generation/collection of associated data (images, annotations, clinical data), ending with training an AI model for the detection of tumor in the images. The proposed model has become an open conceptual foundation of the currently developed ISO 23494 standard on provenance for biotechnology domain.

The Common Provenance Model: Capturing Distributed Provenance in Life Sciences Processes.

The distributed nature of modern research emphasizes the importance of collecting and sharing the history of digital and physical material, to improve the reproducibility of experiments and the quality and reusability of results. Yet, the application of the current methodologies to record provenance information is largely scattered, leading to silos of provenance information at different granularities. To tackle this fragmentation, we developed the Common Provenance Model, a set of guidelines for the generation of interoperable provenance information, and to allow the reconstruction and the navigation of a continuous provenance chain. This work presents the first version of the model, available online, based on the W3C PROV Data Model and the Provenance Composition pattern.

Prognostic Utility of the Gleason Grading System Revisions and Histopathological Factors Beyond Gleason Grade.

BACKGROUND: The International Society of Urological Pathology (ISUP) revised the Gleason system in 2005 and 2014. The impact of these changes on prostate cancer (PCa) prognostication remains unclear. OBJECTIVE: To evaluate if the ISUP 2014 Gleason score (GS) predicts PCa death better than the pre-2005 GS, and if additional histopathological information can further improve PCa death prediction. PATIENTS AND METHODS: We conducted a case-control study nested among men in the National Prostate Cancer Register of Sweden diagnosed with non-metastatic PCa 1998-2015. We included 369 men who died from PCa (cases) and 369 men who did not (controls). Two uro-pathologists centrally re-reviewed biopsy ISUP 2014 Gleason grading, poorly formed glands, cribriform pattern, comedonecrosis, perineural invasion, intraductal, ductal and mucinous carcinoma, percentage Gleason 4, inflammation, high-grade prostatic intraepithelial neoplasia (HGPIN) and post-atrophic hyperplasia. Pre-2005 GS was back-transformed using i) information on cribriform pattern and/or poorly formed glands and ii) the diagnostic GS from the registry. Models were developed using Firth logistic regression and compared in terms of discrimination (AUC). RESULTS: The ISUP 2014 GS (AUC = 0.808) performed better than the pre-2005 GS when back-transformed using only cribriform pattern (AUC = 0.785) or both cribriform and poorly formed glands (AUC = 0.792), but not when back-transformed using only poorly formed glands (AUC = 0.800). Similarly, the ISUP 2014 GS performed better than the diagnostic GS (AUC = 0.808 vs 0.781). Comedonecrosis (AUC = 0.811), HGPIN (AUC = 0.810) and number of cores with ≥50% cancer (AUC = 0.810) predicted PCa death independently of the ISUP 2014 GS. CONCLUSION: The Gleason Grading revisions have improved PCa death prediction, likely due to classifying cribriform patterns, rather than poorly formed glands, as Gleason 4. Comedonecrosis, HGPIN and number of cores with ≥50% cancer further improve PCa death discrimination slightly.

FAIRifying Clinical Studies Metadata: A Registry for the Biomedical Research.

The data produced during a research project are too often collected for the sole purpose of the study, therefore hindering profitable reuse in similar contexts. The growing need to counteract this trend has recently led to the formalization of the FAIR principles that aim to make (meta)data Findable, Accessible, Interoperable and Reusable, for humans and machines. Since their introduction, efforts are ongoing to encourage FAIR principles adoption and to implement solutions based on them. This paper reports on the FAIR-compliant registry we developed to collect and serve metadata describing clinical trials. The design of the registry is based on the FAIR Data Point (FDP) specifications, the state-of-the-art reference for FAIRified metadata sharing. To map the metadata relevant to our use case, we have extended the DCAT-based semantic model of the FDP adopting well-established ontologies in the biomedical and clinical domain, like the Semanticscience Integrated Ontology (SIO). Current implementation is based on the Molgenis software and provides both a user interface and a REST API for metadata discovering. At present the registry is being loaded with the metadata of the 18 clinical studies included in the 'I FAIR Program', a project finalised to the dissemination of FAIR best practices among the clinical researchers in Sardinia (Italy). After a testing phase, the registry will be publicly available, while the new model and the source code will be released open source.

openEHR Is FAIR-Enabling by Design.

The FAIR Principles are a set of recommendations that aim to underpin knowledge discovery and integration by making the research outcomes Findable, Accessible, Interoperable and Reusable. These guidelines encourage the accurate recording and exchange of data, coupled with contextual information about their creation, expressed in domain-specific standards and machine-readable formats. This paper analyses the potential support to FAIRness of the openEHR specifications and reference implementation, by theoretically assessing their compliance with each of the 15 FAIR principles. Our study highlights how the openEHR approach, thanks to its computable semantics-oriented design, is inherently FAIR-enabling and is a promising implementation strategy for creating FAIR-compliant Clinical Data Repositories (CDRs).

Interchangeability of light and virtual microscopy for histopathological evaluation of prostate cancer.

Virtual microscopy (VM) holds promise to reduce subjectivity as well as intra- and inter-observer variability for the histopathological evaluation of prostate cancer. We evaluated (i) the repeatability (intra-observer agreement) and reproducibility (inter-observer agreement) of the 2014 Gleason grading system and other selected features using standard light microscopy (LM) and an internally developed VM system, and (ii) the interchangeability of LM and VM. Two uro-pathologists reviewed 413 cores from 60 Swedish men diagnosed with non-metastatic prostate cancer 1998-2014. Reviewer 1 performed two reviews using both LM and VM. Reviewer 2 performed one review using both methods. The intra- and inter-observer agreement within and between LM and VM were assessed using Cohen's kappa and Bland and Altman's limits of agreement. We found good repeatability and reproducibility for both LM and VM, as well as interchangeability between LM and VM, for primary and secondary Gleason pattern, Gleason Grade Groups, poorly formed glands, cribriform pattern and comedonecrosis but not for the percentage of Gleason pattern 4. Our findings confirm the non-inferiority of VM compared to LM. The repeatability and reproducibility of percentage of Gleason pattern 4 was poor regardless of method used warranting further investigation and improvement before it is used in clinical practice.

The openEHR Genomics Project.

Current high-throughput sequencing technologies allow us to acquire entire genomes in a very short time and at a relatively sustainable cost, thus resulting in an increasing diffusion of genetic test capabilities, in specialized clinical laboratories and research centers. In contrast, it is still limited the impact of genomic information on clinical decisions, as an effective interpretation is a challenging task. From the technological point of view, genomic data are big in size, have a complex granular nature and strongly depend on the computational steps of the generation and processing workflows. This article introduces our work to create the openEHR Genomic Project and the set of genomic information models we developed to catch such complex structure and to preserve data provenance efficiently in a machine-readable format. The models support clinical actionability of data, by improving their quality, fostering interoperability and laying the basis for re-usability.

Estimation of Relative and Absolute Risks in a Competing-Risks Setting Using a Nested Case-Control Study Design: Example From the ProMort Study.

In this paper, we describe the Prognostic Factors for Mortality in Prostate Cancer (ProMort) study and use it to demonstrate how the weighted likelihood method can be used in nested case-control studies to estimate both relative and absolute risks in the competing-risks setting. ProMort is a case-control study nested within the National Prostate Cancer Register (NPCR) of Sweden, comprising 1,710 men diagnosed with low- or intermediate-risk prostate cancer between 1998 and 2011 who died from prostate cancer (cases) and 1,710 matched controls. Cause-specific hazard ratios and cumulative incidence functions (CIFs) for prostate cancer death were estimated in ProMort using weighted flexible parametric models and compared with the corresponding estimates from the NPCR cohort. We further drew 1,500 random nested case-control subsamples of the NPCR cohort and quantified the bias in the hazard ratio and CIF estimates. Finally, we compared the ProMort estimates with those obtained by augmenting competing-risks cases and by augmenting both competing-risks cases and controls. The hazard ratios for prostate cancer death estimated in ProMort were comparable to those in the NPCR. The hazard ratios for dying from other causes were biased, which introduced bias in the CIFs estimated in the competing-risks setting. When augmenting both competing-risks cases and controls, the bias was reduced.

OpenEHR modeling for genomics in clinical practice.

PURPOSE: The increasing usage of high throughput sequencing in personalized medicine brings new challenges to the realm of healthcare informatics. Patient records need to accommodate data of unprecedented size and complexity as well as keep track of their production process. In this work we present a solution for integrating genomic data into electronic health records via openEHR archetypes. METHODS: We use the popular Variant Call Format as the base format to represent genetic test results within openEHR. We evaluate existing openEHR archetypes to determine what can be extended or specialized and what needs to be developed ex novo. RESULTS: Eleven new archetypes have been developed, while an existing one has been specialized to represent genomic data. We show their applicability to rare genetic diseases and compare our approach to HL7 FHIR. CONCLUSION: The proposed model allows to represent genetic test results in health records in a structured format. It supports different levels of abstraction, allowing both automated processing and clinical decision support. It is extensible via external references, allowing to keep track of data provenance and adapt to future domain changes.