Developing the Quantitative Histopathology Image Ontology : A case study using the hot spot detection problem

Interoperability across data sets is a key challenge for quantitative histopathological imaging. There is a need for an ontology that can support effective merging of pathological image data with associated clinical and demographic data. To foster organized, cross-disciplinary, information-driven collaborations in the pathological imaging field, we propose to develop an ontology to represent imaging data and methods used in pathological imaging and analysis, and call it Quantitative Histopathological Imaging Ontology – QHIO. We apply QHIO to breast cancer hot-spot detection with the goal of enhancing reliability of detection by promoting the sharing of data between image analysts

1st INCF Workshop on Genetic Animal Models for Brain Diseases

The INCF Secretariat organized a workshop to focus on the “role of neuroinformatics in the processes of building, evaluating, and using genetic animal models for brain diseases” in Stockholm, December 13–14, 2009. Eight scientists specialized in the fields of neuroinformatics, database, ontologies, and brain disease participated together with two representatives of the National Institutes of Health and the European Union, as well as three observers of the national INCF nodes of Norway, Poland, and the United Kingdom

Rho-A prenylation and signaling link epithelial homeostasis to intestinal inflammation

Although defects in intestinal barrier function are discussed as a key pathogenic factor in patients with inflammatory bowel diseases (IBD), the molecular pathways driving disease-specific alterations of intestinal epithelial cells (IECs) are largely unknown. Here, we performed a novel approach to characterize the transcriptome of IECs from IBD patients using a genome wide approach. We observed disease-specific alterations in IECs with markedly impaired Rho-A signaling in active IBD patients. Localization of epithelial Rho-A was shifted to the cytosol in IBD where Rho-A activation was suppressed due to reduced expression of the Rho-A prenylation enzyme GGTase-I. The functional relevance of this pathway was highlighted by studies in mice with conditional gene targeting in which deletion of RhoA or GGTase-I in IECs caused spontaneous chronic intestinal inflammation with accumulation of granulocytes and CD4+ T cells. This phenotype was associated with cytoskeleton rearrangement and aberrant cell shedding ultimately leading to loss of epithelial integrity and subsequent inflammation. These findings uncover deficient prenylation of Rho-A as a key player in the pathogenesis of IBD. As therapeutic triggering of Rho-A signaling suppressed intestinal inflammation in mice with GGTase-I deficient IECs, our findings open new avenues for treatment of epithelial injury and mucosal inflammation in IBD patients

Graph Representation Learning in Biomedicine

Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge. With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing, and learning with such networks. In this review, we put forward an observation that long-standing principles of networks in biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces, and capture the breadth of ways in which representation learning is proving useful. Areas of profound impact include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines

TGFβ signaling is associated with changes in inflammatory gene expression and perineuronal net degradation around inhibitory neurons following various neurological insults.

Brain damage due to stroke or traumatic brain injury (TBI), both leading causes of serious long-term disability, often leads to the development of epilepsy. Patients who develop post-injury epilepsy tend to have poor functional outcomes. Emerging evidence highlights a potential role for blood-brain barrier (BBB) dysfunction in the development of post-injury epilepsy. However, common mechanisms underlying the pathological hyperexcitability are largely unknown. Here, we show that comparative transcriptome analyses predict remodeling of extracellular matrix (ECM) as a common response to different types of injuries. ECM-related transcriptional changes were induced by the serum protein albumin via TGFβ signaling in primary astrocytes. In accordance with transcriptional responses, we found persistent degradation of protective ECM structures called perineuronal nets (PNNs) around fast-spiking inhibitory interneurons, in a rat model of TBI as well as in brains of human epileptic patients. Exposure of a naïve brain to albumin was sufficient to induce the transcriptional and translational upregulation of molecules related to ECM remodeling and the persistent breakdown of PNNs around fast-spiking inhibitory interneurons, which was contingent on TGFβ signaling activation. Our findings provide insights on how albumin extravasation that occurs upon BBB dysfunction in various brain injuries can predispose neural circuitry to the development of chronic inhibition deficits

A sustainable visual representation of available histopathological digital knowledge for breast cancer grading

Background Recently, anatomic pathology (AP) has seen the introduction of several tools such as slide scanners and virtual slide technologies, creating the conditions for broader adoption of computer aided diagnosis based on whole slide images (WSI). This change brings up a number of new scientific challenges such as the sustainable management of the explicit and unambiguous semantics associated to the diagnostic interpretation of AP images by both humans (pathologists) and computers (image analysis algorithms) . In order to reduce inter-observer variability between AP reports of malignant tumors, the College of American Pathologists edited more than 60 organ-specific Cancer Checklists and associated Protocols (CAP-CC&P). Each checklist includes a set of AP observations that are expected to be reported by pathologists in organ-specific AP cancer reports. Our objective was to i) identify the available histopathological formalized knowledge from NCBO Bioportal and UMLS metathesaurus in the scope of the CAP CC&P for breast cancer grading and ii) to build a sustainable visual representation of this knowledge using UMLS semantic types. Methods Our methodology was applied on the two breast cancer CAP-CC&Ps dedicated to invasive carcinoma (IC) and ductal carcinoma in situ (DCIS). We focused on a subset of quantifiable AP observations of the CAP-CCs - i.e. observable entities that could be computed by image analysis tools and on the corresponding notes in the protocols that unambiguously describe how pathologists should derive a high-level observation (e.g. Nottingham score) from low-level morphological characteristics observed in images (e.g. mitotic count or glandular/tubular differentiation).The notes were annotated manually by two AP experts (gold standard) and automatically by NCBO Annotator using the 508 ontologies available on the NCBO platform. A sub-set of reference ontologies was selected based on their capacities to automatically identify concepts in the notes and compared to the subset of ontologies selected based on their capacity to identify the concepts identified by experts (gold standard). Once automatically extracted from the notes, the concepts belonging to different ontologies, were integrated into a unique graph and organized according to UMLS semantic types. Results The most relevant biomedical ontologies to be used for the annotation of the notes describing quantifiable observable entities of breast cancer CAP-CC&Ps are SNOMED-CT, LOINC, NCIT, NCI CaDSR Value Sets and PathLex. A visual representation integrating 25 concepts from the 5 different ontologies organized according to 11 UMLS semantic types was built to support AP experts for building a formal representation of the low-level quantifiable entities automatically extracted from the CAP-CC&Ps notes. Conclusion The proposed approach and tools, based on the CAP-CC&Ps, aim at supporting AP experts in building a standard-based representation of low-level morphological abnormalities observed in cancer that can be quantified using image analysis tools. This effort is complementary to the Integrating the Healthcare Enterprise (IHE) initiative building a standard-based representation of high-level AP observations required in cancer AP reports. Additional efforts are needed to achieve a workable standard-based formal representation of histopathological knowledge integrating both observable entities reported by humans (pathologists) and quantifiable entities automatically computed by machines. Providing such unique formal representation paves the way for more efficient use of computer aided diagnosis in AP as well as for the development of new biomarkers based on automatic analysis of whole slide images (WSI)

Time-efficient sparse analysis of histopathological Whole Slide Images

International audienceHistopathological examination is a powerful method for the prognosis of critical diseases. But, despite significant advances in high-speed and high-resolution scanning devices or in virtual exploration capabilities, the clinical analysis of Whole Slide Images (WSI) largely remains the work of human experts. We propose an innovative platform in which multi-scale computer vision algorithms perform fast analysis of a histopathological WSI. It relies on specific high and generic low resolution image analysis algorithms embedded in a multi-scale framework to rapidly identify the high power fields of interest used by the pathologist to assess a global grading. GPU technologies as well speed up the global time-efficiency of the system. In a sense, sparse coding and sampling is the keystone of our approach. In terms of validation, we are designing a computer-aided breast biopsy analysis application based on histopathology images and designed in collaboration with a pathology department. The current ground truth slides correspond to about 36,000 high magnification (40X) high power fields. The time processing to achieve automatic WSI analysis is on a par with the pathologist's performance (about ten minutes a WSI), which constitutes by itself a major contribution of the proposed methodology