Kostas Marias

The advancements in healthcare practice have brought to the fore the need for flexible access to health-related information and created an ever-growing demand for the design and the development of data management infrastructures for translational and personalized medicine. In this paper, we present the data management solution implemented for the MyHealthAvatar EU research project, a project that attempts to create a digital representation of a patient's health status. The platform is capable of aggregating several knowledge sources relevant for the provision of individualized personal services. To this end, state of the art technologies are exploited, such as ontologies to model all available information, semantic integration to enable data and query translation and a variety of linking services to allow connecting to external sources. All original information is stored in a NoSQL database for reasons of efficiency and fault tolerance. Then it is semantically uplifted through a semantic warehouse which enables efficient access to it. All different technologies are combined to create a novel web-based platform allowing seamless user interaction through APIs that support personalized, granular and secure access to the relevant information.

Patients today have ample opportunities to inform themselves about their disease and possible treatments using the Internet. While this type of patient empowerment is widely regarded as having a positive influence on the treatment, there exists the problem that the quality of information that can be found on online is very diverse. This paper presents a platform which empowers patients by allowing searching in a high quality document repository. In addition, it automatically provides intelligent and personalized recommendations according to the individual preferences and medical conditions.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of contrast leakage from the vascular tissue by using pharmacokinetic (PK) models. Such quantitative analysis of DCE-MRI data provides physiological parameters that are able to provide information of tumor pathophysiology and therapeutic outcome. Several assumptive PK models have been proposed to characterize microcirculation in the tumoral tissue. In this paper, we present a comparative study between the well-known extended Tofts model (ETM) and the more recent gamma capillary transit time (GCTT) model, with the latter showing initial promising results in the literature. To enhance the GCTT imaging biomarkers, we introduce a novel method for segmenting the tumor area into subregions according to their vascular heterogeneity characteristics. A cohort of 11 patients diagnosed with glioblastoma multiforme with known therapeutic outcome was used to assess the predictive value of both models in terms of correctly classifying responders and nonresponders based on only one DCE-MRI examination. The results indicate that GCTT model's PK parameters perform better than those of ETM, while the segmentation of the tumor regions of interest based on vascular heterogeneity further enhances the discriminatory power of the GCTT model.

ABSTRACT Cancer cells inefficiently produce energy through glycolysis even in ample oxygen, a phenomenon known as “aerobic glycolysis”. A characteristic of the rapid and incomplete catabolism of glucose is the secretion of lactate. Genome-scale metabolic models have been recently employed to describe the glycolytic phenotype of highly proliferating human cancer cells. Genome-scale models describe genotype-phenotype relations revealing the full extent of metabolic capabilities of genotypes under various environmental conditions. The importance of these approaches in understanding some aspects of cancer complexity, as well as in cancer diagnostics and individualized therapeutic schemes related to metabolism is evident. Based on previous metabolic models, we explore the metabolic capabilities and rerouting that occur in cancer metabolism when we apply a strategy that allows near optimal growth solution while maximizing lactate secretion. The simulations show that slight deviations around the optimal growth are sufficient for adequate lactate release and that glucose uptake and lactate secretion are correlated at high proliferation rates as it has been observed. Inhibition of lactate dehydrogenase-A, an enzyme involved in the conversion of pyruvate to lactate, substantially reduces lactate release. We also observe that activating specific reactions associated with the migration-related PLCγ enzyme, the proliferation rate decreases. Furthermore, we incorporate flux constraints related to differentially expressed genes in Glioblastoma Multiforme in an attempt to construct a Glioblastoma-specific metabolic model and investigate its metabolic capabilities across different glucose uptake bounds.

Neuroimaging plays a crucial role in diagnosis of brain tumors and in the decision-making process for therapy. Functional imaging techniques can reflect cellular density (diffusion imaging), capillary density (perfusion techniques), and tissue biochemistry (magnetic resonance [MR] spectroscopy). In addition, cortical activation imaging (functional MR imaging) can identify various loci of eloquent cerebral cortical function. Combining these new tools can increase diagnostic specificity and confidence. Familiarity with conventional and advanced imaging findings facilitates accurate diagnosis, differentiation from other processes, and optimal patient treatment. This article is a practical synopsis of pathologic, clinical, and imaging spectra of most common brain tumors.

This paper describes a flexible and easy-to-use annotation platform (GUI) for quick and precise identification and delineation of tumors in medical images. The design of the platform is clinically driven in order to ensure that the clinician can efficiently and intuitively annotate large number of 3D tomographic datasets. Both manual and well-known semiautomatic segmentation techniques are available in the platform allowing clinician to annotate multiple regions of interest at the same session. Additionally, it includes contour drawing, refinement and labeling tools that can effectively assist in the delineation of tumors. Furthermore, segmented tumor regions can be annotated, labeled, deleted, added and redefined. The platform has been tested over several MRI datasets to assess usability, extensibility and robustness with promising results.

... Biol Psychiatry 57, (8) 873-884 (2005) Lu M, Mitsias PD, Ewing JR, Soltanian-Zadeh H,Bagher-Ebadian H, Zhao QM, Oja-Tebbe N, Patel SC, Chopp M. Henry Ford Hlth Sci Ctr, Dept Biostat & Res Epidemiol, 1 Ford Pl 3E, Detroit, Mi 48202, USA. ...

The "Oncosimulator" is at the same time a concept of multilevel integrative cancer and (treatment affected) normal tissue biology, an algorithmic construct and a software tool which aims at supporting the clinician in the process of optimizing cancer treatment on the patient individualized basis. Additionally it is a platform for better understanding and exploring the natural phenomenon of cancer as well as training doctors and interested patients alike. In order to achieve all of these goals it has to undergo a thorough clinical optimization and validation process. This is one of the goals of the European Commission funded integrated project "ACGT: Advancing Clinicogenomic Trials on Cancer". Nephroblastoma (Wilms' tumor) and breast cancer have been selected to serve as two paradigms to clinically specify and evaluate the "Oncosimulator" as well as the emerging domain of in silico oncology.

In this paper, we emphasise the need to establish correspondences between regions in temporal mammograms for robust and more accurate registration. Based on automatically detected boundary landmarks, we partially register, then subsequently analyse the mammogram pair using a non-linear wavelet scale-space to isolate significant regions of interest. We show that a usually small, but significant number of internal correspondences greatly improves registration and better approximates the complex internal tissue deformation due mainly to differences in compression.

In this paper, we explore the idea of quantifying local breast-tissue density changes. Breast tissue density has been correlated to breast cancer incidence in numerous studies which have shown a statistical relationship between glandular density and the occurrence of cancer. In particular, postmenopausal women who take HRT run an increased risk of developing cancer due to the "regeneration" of fibroglandular tissue that is often induced by the exogenous hormones. In this paper, we present a method that combines mammogram normalisation and volume-preserving registration, and which can be the starting point for temporal-local breast tissue quantification.

A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Modeling tumor growth has proven a very challenging problem, mainly due to the fact that tumors are highly complex systems that involve dynamic interactions spanning multiple scales both in time and space. The desire to describe interactions in various scales has given rise to modeling approaches that use both continuous and discrete variables, known as hybrid approaches. This work refers to a hybrid model on a 2D square lattice focusing on cell movement dynamics as they play an important role in tumor morphology, invasion and metastasis and are considered as indicators for the stage of malignancy used for early prognosis and effective treatment. Considering various distributions of the microenvironment, we explore how Neumann vs. Moore neighborhood schemes affects tumor growth and morphology. The results indicate that the importance of neighborhood selection is critical under specific conditions that include i) increased hapto/chemo-tactic coefficient, ii) a rugged microenvironment and iii) ECM degradation.

This paper presents the needs and requirements that led to the formation of the ACGT (Advancing Clinico Genomic Trials) integrated project, its vision and methodological approaches of the project. The ultimate objective of the ACGT project is the development of a European biomedical grid for cancer research, based on the principles of open access and open source, enhanced by a

Tumour growth and response to radiotherapeutic schemes is a markedly multiscale process which by no means can be reduced to only molecular or cellular events. Within this framework a new scientific area, i.e. in silico oncology has been proposed in order to address the previously mentioned hypercomplex process at essentially all levels of biocomplexity. This paper focuses on the case

The crucial role of imaging biomarkers is sparsely mentioned in the literature due to the complex nature of medical images, the interpretation variability and the multidisciplinary approach needed to extract, validate, and translate such biomarkers to the clinical setting. ...