T-regulatory cells predict clinical outcome in soft tissue sarcoma patients: a clinico-pathological study.

BACKGROUND: Soft tissue sarcomas (STS) are generally considered non-immunogenic, although specific subtypes respond to immunotherapy. Antitumour response within the tumour microenvironment relies on a balance between inhibitory and activating signals for tumour-infiltrating lymphocytes (TILs). This study analysed TILs and immune checkpoint molecules in STS, and assessed their prognostic impact regarding local recurrence (LR), distant metastasis (DM), and overall survival (OS). METHODS: One-hundred and ninety-two surgically treated STS patients (median age: 63.5 years; 103 males [53.6%]) were retrospectively included. Tissue microarrays were constructed, immunohistochemistry for PD-1, PD-L1, FOXP3, CD3, CD4, and CD8 performed, and staining assessed with multispectral imaging. TIL phenotype abundance and immune checkpoint markers were correlated with clinical and outcome parameters (LR, DM, and OS). RESULTS: Significant differences between histology and all immune checkpoint markers except for FOXP3+ and CD3-PD-L1+ cell subpopulations were found. Higher levels of PD-L1, PD-1, and any TIL phenotype were found in myxofibrosarcoma as compared to leiomyosarcoma (all p < 0.05). The presence of regulatory T cells (Tregs) was associated with increased LR risk (p = 0.006), irrespective of margins. Other TILs or immune checkpoint markers had no significant impact on outcome parameters. CONCLUSIONS: TIL and immune checkpoint marker levels are most abundant in myxofibrosarcoma. High Treg levels are independently associated with increased LR risk, irrespective of margins.

[Big data, medical language and biomedical terminology systems]. / Big Data, medizinische Sprache und biomedizinische Ordnungssysteme.

A variety of rich terminology systems, such as thesauri, classifications, nomenclatures and ontologies support information and knowledge processing in health care and biomedical research. Nevertheless, human language, manifested as individually written texts, persists as the primary carrier of information, in the description of disease courses or treatment episodes in electronic medical records, and in the description of biomedical research in scientific publications. In the context of the discussion about big data in biomedicine, we hypothesize that the abstraction of the individuality of natural language utterances into structured and semantically normalized information facilitates the use of statistical data analytics to distil new knowledge out of textual data from biomedical research and clinical routine. Computerized human language technologies are constantly evolving and are increasingly ready to annotate narratives with codes from biomedical terminology. However, this depends heavily on linguistic and terminological resources. The creation and maintenance of such resources is labor-intensive. Nevertheless, it is sensible to assume that big data methods can be used to support this process. Examples include the learning of hierarchical relationships, the grouping of synonymous terms into concepts and the disambiguation of homonyms. Although clear evidence is still lacking, the combination of natural language technologies, semantic resources, and big data analytics is promising.

Cross-domain targeted ontology subsets for annotation: the case of SNOMED CORE and RxNorm.

The benefits of using ontology subsets versus full ontologies are well-documented for many applications. In this study, we propose an efficient subset extraction approach for a domain using a biomedical ontology repository with mappings, a cross-ontology, and a source subset from a related domain. As a case study, we extracted a subset of drugs from RxNorm using the UMLS Metathesaurus, the NDF-RT cross-ontology, and the CORE problem list subset of SNOMED CT. The extracted subset, which we termed RxNorm/CORE, was 4% the size of the full RxNorm (0.4% when considering ingredients only). For evaluation, we used CORE and RxNorm/CORE as thesauri for the annotation of clinical documents and compared their performance to that of their respective full ontologies (i.e., SNOMED CT and RxNorm). The wide range in recall of both CORE (29-69%) and RxNorm/CORE (21-35%) suggests that more quantitative research is needed to assess the benefits of using ontology subsets as thesauri in annotation applications. Our approach to subset extraction, however, opens a door to help create other types of clinically useful domain specific subsets and acts as an alternative in scenarios where well-established subset extraction techniques might suffer from difficulties or cannot be applied.

Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.

Purpose: Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs. Approach: We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images. Results: We could generate Cell2Grid images at 5 - µ m resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( p < 0.0001 ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to 5 µ m with bilinear interpolation. Compared with images at 1 - µ m resolution (bilinear rescaling), our method reduced CNN training time by 85%. Conclusions: Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation.

Insights into the Composition of a Co-Culture of 10 Probiotic Strains (OMNi BiOTiC® AAD10) and Effects of Its Postbiotic Culture Supernatant.

BACKGROUND: We aimed to gain insights in a co-culture of 10 bacteria and their postbiotic supernatant. METHODS: Abundances and gene expression were monitored by shotgun analysis. The supernatant was characterized by liquid chromatography mass spectroscopy (LC-MS) and gas chromatography mass spectroscopy (GC-MS). Supernatant was harvested after 48 h (S48) and 196 h (S196). Susceptibility testing included nine bacteria and C. albicans. Bagg albino (BALBc) mice were fed with supernatant or culture medium. Fecal samples were obtained for 16S analysis. RESULTS: A time-dependent decrease of the relative abundances and gene expression of L. salivarius, L. paracasei, E. faecium and B. longum/lactis and an increase of L. plantarum were observed. Substances in LC-MS were predominantly allocated to groups amino acids/peptides/metabolites and nucleotides/metabolites, relating to gene expression. Fumaric, panthotenic, 9,3-methyl-2-oxovaleric, malic and aspartic acid, cytidine monophosphate, orotidine, phosphoserine, creatine, tryptophan correlated to culture time. Supernatant had no effect against anaerobic bacteria. S48 was reactive against S. epidermidis, L. monocytogenes, P. aeruginosae, E. faecium and C. albicans. S196 against S. epidermidis and Str. agalactiae. In vivo S48/S196 had no effect on alpha/beta diversity. Linear discriminant analysis effect size (LEfSe) and analysis of composition of microbiomes (ANCOM) revealed an increase of Anaeroplasma and Faecalibacterium prausnitzii. CONCLUSIONS: The postbiotic supernatant had positive antibacterial and antifungal effects in vitro and promoted the growth of distinct bacteria in vivo.

DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals-Planification, Implementation, and Preliminary Results.

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

Influence of tumor-infiltrating immune cells on local control rate, distant metastasis, and survival in patients with soft tissue sarcoma.

Soft tissue sarcomas (STS) are considered non-immunogenic, although distinct entities respond to anti-tumor agents targeting the tumor microenvironment. This study's aims were to investigate relationships between tumor-infiltrating immune cells and patient/tumor-related factors, and assess their prognostic value for local recurrence (LR), distant metastasis (DM), and overall survival (OS). One-hundred-eighty-eight STS-patients (87 females [46.3%]; median age: 62.5 years) were retrospectively analyzed. Tissue microarrays (in total 1266 cores) were stained with multiplex immunohistochemistry and analyzed with multispectral imaging. Seven cell types were differentiated depending on marker profiles (CD3+, CD3+ CD4+ helper, CD3+ CD8+ cytotoxic, CD3+ CD4+ CD45RO+ helper memory, CD3+ CD8+ CD45RO+ cytotoxic memory T-cells; CD20 + B-cells; CD68+ macrophages). Correlations between phenotype abundance and variables were analyzed. Uni- and multivariate Fine&Gray and Cox-regression models were constructed to investigate prognostic variables. Model calibration was assessed with C-index. IHC-findings were validated with TCGA-SARC gene expression data of genes specific for macrophages, T- and B-cells. B-cell percentage was lower in patients older than 62.5 years (p = .013), whilst macrophage percentage was higher (p = .002). High B-cell (p = .035) and macrophage levels (p = .003) were associated with increased LR-risk in the univariate analysis. In the multivariate setting, high macrophage levels (p = .014) were associated with increased LR-risk, irrespective of margins, age, gender or B-cells. Other immune cells were not associated with outcome events. High macrophage levels were a poor prognostic factor for LR, irrespective of margins, B-cells, gender and age. Thus, anti-tumor, macrophage-targeting agents may be applied more frequently in tumors with enhanced macrophage infiltration.

Building SNOMED CT Post-Coordinated Expressions from Annotation Groups.

SNOMED CT supports post-coordination, a technique to combine clinical concepts to ontologically define more complex concepts. This technique follows the validity restrictions defined in the SNOMED CT Concept Model. Pre-coordinated expressions are compositional expressions already in SNOMED CT, whereas post-coordinated expressions extend its content. In this project we aim to evaluate the suitability of existing pre-coordinated expressions to provide the patterns for composing typical clinical information based on a defined list of sets of interrelated SNOMED CT concepts. The method produces a 9.3% precision and a 95.9% recall. As a consequence, further investigations are needed to develop heuristics for the selection of the most meaningful matched patterns to improve the precision.

Can SNOMED CT be squeezed without losing its shape?

BACKGROUND: In biomedical applications where the size and complexity of SNOMED CT become problematic, using a smaller subset that can act as a reasonable substitute is usually preferred. In a special class of use cases-like ontology-based quality assurance, or when performing scaling experiments for real-time performance-it is essential that modules show a similar shape than SNOMED CT in terms of concept distribution per sub-hierarchy. Exactly how to extract such balanced modules remains unclear, as most previous work on ontology modularization has focused on other problems. In this study, we investigate to what extent extracting balanced modules that preserve the original shape of SNOMED CT is possible, by presenting and evaluating an iterative algorithm. METHODS: We used a graph-traversal modularization approach based on an input signature. To conform to our definition of a balanced module, we implemented an iterative algorithm that carefully bootstraped and dynamically adjusted the signature at each step. We measured the error for each sub-hierarchy and defined convergence as a residual sum of squares <1. RESULTS: Using 2000 concepts as an initial signature, our algorithm converged after seven iterations and extracted a module 4.7 % the size of SNOMED CT. Seven sub-hierarhies were either over or under-represented within a range of 1-8 %. CONCLUSIONS: Our study shows that balanced modules from large terminologies can be extracted using ontology graph-traversal modularization techniques under certain conditions: that the process is repeated a number of times, the input signature is dynamically adjusted in each iteration, and a moderate under/over-representation of some hierarchies is tolerated. In the case of SNOMED CT, our results conclusively show that it can be squeezed to less than 5 % of its size without any sub-hierarchy losing its shape more than 8 %, which is likely sufficient in most use cases.

Structural Patterns under X-Rays: Is SNOMED CT Growing Straight?

Unprincipled modeling decisions in large-domain ontologies, such as SNOMED CT, are problematic and might act as a barrier for their quality assurance and successful use in electronic health records. Most previous work has focused on clustering problematic concepts, which is helpful for quality control but faces difficulties in pinpointing the origin of those modeling problems. In this study, we examined the underlying structural patterns in SNOMED CT's data model as such patterns directly reflect the modeling strategies of editors. Our results showed that 92% of all structural patterns found accumulated in the Procedure and Clinical finding sub-hierarchies, and pattern reuse was low; over 30% of patterns were only used once. A qualitative analysis of a sample of 50 such singleton patterns revealed modeling problems, including redundancy, omission, and inconsistency. The problems detected in the sample suggest that the analysis of structural patterns is a valuable technique for revealing problematic areas of SNOMED CT and modeling the styles of terminology editors. Furthermore, the patterns that describe the modeling of a large number of concepts could provide insights for template creation and refinement in SNOMED CT.

SEMCARE: Multilingual Semantic Search in Semi-Structured Clinical Data.

The vast amount of clinical data in electronic health records constitutes a great potential for secondary use. However, most of this content consists of unstructured or semi-structured texts, which is difficult to process. Several challenges are still pending: medical language idiosyncrasies in different natural languages, and the large variety of medical terminology systems. In this paper we present SEMCARE, a European initiative designed to minimize these problems by providing a multi-lingual platform (English, German, and Dutch) that allows users to express complex queries and obtain relevant search results from clinical texts. SEMCARE is based on a selection of adapted biomedical terminologies, together with Apache UIMA and Apache Solr as open source state-of-the-art natural language pipeline and indexing technologies. SEMCARE has been deployed and is currently being tested at three medical institutions in the UK, Austria, and the Netherlands, showing promising results in a cardiology use case.

Usability-driven pruning of large ontologies: the case of SNOMED CT.

OBJECTIVES: To study ontology modularization techniques when applied to SNOMED CT in a scenario in which no previous corpus of information exists and to examine if frequency-based filtering using MEDLINE can reduce subset size without discarding relevant concepts. MATERIALS AND METHODS: Subsets were first extracted using four graph-traversal heuristics and one logic-based technique, and were subsequently filtered with frequency information from MEDLINE. Twenty manually coded discharge summaries from cardiology patients were used as signatures and test sets. The coverage, size, and precision of extracted subsets were measured. RESULTS: Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Pre-computed subsets and logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. DISCUSSION: Extracting subsets to annotate discharge summaries is challenging when no previous corpus exists. Ontology modularization provides valuable techniques, but the resulting modules grow as signatures spread across subhierarchies, yielding a very low precision. CONCLUSION: Graph-traversal strategies and frequency data from an authoritative source can prune large biomedical ontologies and produce useful subsets that still exhibit acceptable coverage. However, a clinical corpus closer to the specific use case is preferred when available.