Kostas Marias | Foundation for Research and Technology - Hellas (original) (raw)

Papers by Kostas Marias

Research paper thumbnail of A supportive environment for the long term management of knee osteoarthritis condition

Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies", 2015

Research paper thumbnail of MyHealthAvatar platform: matching real life patients with the generated virtual profiles from in silico clinical trials

Objectives: MyHealthAvatar (MHA) platform aims towards a collaborative partnership among patients... more Objectives: MyHealthAvatar (MHA) platform aims towards a collaborative partnership among patients and healthcare providers [1]. Nowadays, in silico clinical trials (ISCTs), population pharmacokinetics, pharmacogenomics and information communication technologies have provided several tools towards stratified and personalized medicine approaches [2-4]. In this work a methodology of potential fitting of results generated through ISCTs with real life patients through virtual profiles of MHA is presented. To this respect, we use a simple example of discontinuation of warfarin administration during pre-operative period for a 55 year’s old male patient with a MHA profile. Methods: MHA’s architecture is based on integration of multiscale data gained from several sources (i.e. demographic, biomedical, genomics, lifestyle) and transform them into a representation of health status as a “virtual twin” or avatar [1]. The integration of these information from different avatars can lead in a creat...

Research paper thumbnail of Digital patient: Personalized and translational data management through the MyHealthAvatar EU project

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

The advancements in healthcare practice have brought to the fore the need for flexible access to ... more 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.

Research paper thumbnail of Digital patient: Personalized and translational data management through the MyHealthAvatar EU project

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

The advancements in healthcare practice have brought to the fore the need for flexible access to ... more 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.

Research paper thumbnail of A fully automated image analysis framework for quantitative assessment of temporal tumor changes

Research paper thumbnail of Patient Empowerment through Personal Medical Recommendations

Studies in health technology and informatics, 2015

Patients today have ample opportunities to inform themselves about their disease and possible tre... more 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.

Research paper thumbnail of Magnetic relaxation measurements on tissue mimicking phantoms: comparison between different fitting algorithms in MRI T2 calculations

Research paper thumbnail of Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data

Cancer Informatics, 2015

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of cont... more 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.

Research paper thumbnail of Assessing the role of quantitative analysis of mammograms in describing breast density changes in women using HRT

Digital Mammography, 2003

Research paper thumbnail of MRI – Mammography 2D/3D Data Fusion for Breast Pathology Assessment

Lecture Notes in Computer Science, 2000

Research paper thumbnail of The effects of near optimal growth solutions in genome-scale human cancer metabolic model

2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012

ABSTRACT Cancer cells inefficiently produce energy through glycolysis even in ample oxygen, a phe... more 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.

Research paper thumbnail of EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism

Fields Institute Communications, 2012

ABSTRACT

Research paper thumbnail of Imaging of brain tumors

Surgical oncology clinics of North America, 2014

Neuroimaging plays a crucial role in diagnosis of brain tumors and in the decision-making process... more 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.

Research paper thumbnail of DoctorEye: A multifunctional open platform for fast annotation and visualization of tumors in medical images

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009

This paper describes a flexible and easy-to-use annotation platform (GUI) for quick and precise i... more 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.

Research paper thumbnail of Exploring the Competition between Proliferative and Invasive Cancer Phenotypes in a Continuous Spatial Model

Research paper thumbnail of Current awareness in NMR in biomedicine

... Biol Psychiatry 57, (8) 873-884 (2005) Lu M, Mitsias PD, Ewing JR, Soltanian-Zadeh H,Bagher-E... more ... 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. ...

Research paper thumbnail of Multi-Level Image Analysis for Extracting Pathophysiological Parameters Related to Cancer Modeling

Research paper thumbnail of Facial Signs and Psycho-physical Status Estimation for Well-being Assessment

Research paper thumbnail of Temporal mass detection

Research paper thumbnail of An innovative mathematical analysis of routine MRI scans in patients with glioblastoma using DoctorEye

IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012, 2012

ABSTRACT Improving the initial diagnosis and the assessment of response to treatment in malignant... more ABSTRACT Improving the initial diagnosis and the assessment of response to treatment in malignant gliomas, while avoiding invasive methods as much as justifiable, is one major aspect actual research is focusing on. Imaging studies are used to calculate tumor volume and define vital, necrotic and cystic areas within a tumor. Though the visual interpretation of magnetic resonance (MR) images is based on qualitative observation of variation in signal intensity, a correlation of signal intensities with histological features of a tumor is not possible. Better methods are needed for a reliable interpretation of follow-up studies in single patients. Histograms of signal intensities might serve as a method adding quantitative data to the description of a tumor. Using DoctorEye software, tumors can be easily rendered and histograms of the signal intensities within a tumor as well as mean and median signal intensities are possible to calculate. Our results in glioblastoma suggest that these histograms are an innovative method of gaining new tumor-specific information without performing additional investigations in a patient. It can be an additional diagnostic tool in differentiating various intracranial lesions from each other, as well as in assessing response to treatment or progression of malignant glioma.

Research paper thumbnail of A supportive environment for the long term management of knee osteoarthritis condition

Proceedings of the 5th EAI International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies", 2015

Research paper thumbnail of MyHealthAvatar platform: matching real life patients with the generated virtual profiles from in silico clinical trials

Objectives: MyHealthAvatar (MHA) platform aims towards a collaborative partnership among patients... more Objectives: MyHealthAvatar (MHA) platform aims towards a collaborative partnership among patients and healthcare providers [1]. Nowadays, in silico clinical trials (ISCTs), population pharmacokinetics, pharmacogenomics and information communication technologies have provided several tools towards stratified and personalized medicine approaches [2-4]. In this work a methodology of potential fitting of results generated through ISCTs with real life patients through virtual profiles of MHA is presented. To this respect, we use a simple example of discontinuation of warfarin administration during pre-operative period for a 55 year’s old male patient with a MHA profile. Methods: MHA’s architecture is based on integration of multiscale data gained from several sources (i.e. demographic, biomedical, genomics, lifestyle) and transform them into a representation of health status as a “virtual twin” or avatar [1]. The integration of these information from different avatars can lead in a creat...

Research paper thumbnail of Digital patient: Personalized and translational data management through the MyHealthAvatar EU project

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

The advancements in healthcare practice have brought to the fore the need for flexible access to ... more 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.

Research paper thumbnail of Digital patient: Personalized and translational data management through the MyHealthAvatar EU project

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015

The advancements in healthcare practice have brought to the fore the need for flexible access to ... more 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.

Research paper thumbnail of A fully automated image analysis framework for quantitative assessment of temporal tumor changes

Research paper thumbnail of Patient Empowerment through Personal Medical Recommendations

Studies in health technology and informatics, 2015

Patients today have ample opportunities to inform themselves about their disease and possible tre... more 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.

Research paper thumbnail of Magnetic relaxation measurements on tissue mimicking phantoms: comparison between different fitting algorithms in MRI T2 calculations

Research paper thumbnail of Assessing Treatment Response Through Generalized Pharmacokinetic Modeling of DCE-MRI Data

Cancer Informatics, 2015

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) enables the quantification of cont... more 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.

Research paper thumbnail of Assessing the role of quantitative analysis of mammograms in describing breast density changes in women using HRT

Digital Mammography, 2003

Research paper thumbnail of MRI – Mammography 2D/3D Data Fusion for Breast Pathology Assessment

Lecture Notes in Computer Science, 2000

Research paper thumbnail of The effects of near optimal growth solutions in genome-scale human cancer metabolic model

2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), 2012

ABSTRACT Cancer cells inefficiently produce energy through glycolysis even in ample oxygen, a phe... more 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.

Research paper thumbnail of EEG Based Biomarker Identification Using Graph-Theoretic Concepts: Case Study in Alcoholism

Fields Institute Communications, 2012

ABSTRACT

Research paper thumbnail of Imaging of brain tumors

Surgical oncology clinics of North America, 2014

Neuroimaging plays a crucial role in diagnosis of brain tumors and in the decision-making process... more 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.

Research paper thumbnail of DoctorEye: A multifunctional open platform for fast annotation and visualization of tumors in medical images

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009

This paper describes a flexible and easy-to-use annotation platform (GUI) for quick and precise i... more 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.

Research paper thumbnail of Exploring the Competition between Proliferative and Invasive Cancer Phenotypes in a Continuous Spatial Model

Research paper thumbnail of Current awareness in NMR in biomedicine

... Biol Psychiatry 57, (8) 873-884 (2005) Lu M, Mitsias PD, Ewing JR, Soltanian-Zadeh H,Bagher-E... more ... 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. ...

Research paper thumbnail of Multi-Level Image Analysis for Extracting Pathophysiological Parameters Related to Cancer Modeling

Research paper thumbnail of Facial Signs and Psycho-physical Status Estimation for Well-being Assessment

Research paper thumbnail of Temporal mass detection

Research paper thumbnail of An innovative mathematical analysis of routine MRI scans in patients with glioblastoma using DoctorEye

IEEE 12th International Conference on BioInformatics and BioEngineering, BIBE 2012, 2012

ABSTRACT Improving the initial diagnosis and the assessment of response to treatment in malignant... more ABSTRACT Improving the initial diagnosis and the assessment of response to treatment in malignant gliomas, while avoiding invasive methods as much as justifiable, is one major aspect actual research is focusing on. Imaging studies are used to calculate tumor volume and define vital, necrotic and cystic areas within a tumor. Though the visual interpretation of magnetic resonance (MR) images is based on qualitative observation of variation in signal intensity, a correlation of signal intensities with histological features of a tumor is not possible. Better methods are needed for a reliable interpretation of follow-up studies in single patients. Histograms of signal intensities might serve as a method adding quantitative data to the description of a tumor. Using DoctorEye software, tumors can be easily rendered and histograms of the signal intensities within a tumor as well as mean and median signal intensities are possible to calculate. Our results in glioblastoma suggest that these histograms are an innovative method of gaining new tumor-specific information without performing additional investigations in a patient. It can be an additional diagnostic tool in differentiating various intracranial lesions from each other, as well as in assessing response to treatment or progression of malignant glioma.