Publications by authors named "Themis P Exarchos"

64 Publications

A biomarker for lymphoma development in Sjogren's syndrome: Salivary gland focus score.

J Autoimmun 2021 Jul 21;121:102648. Epub 2021 May 21.

Department of Pathophysiology, School of Medicine, National and Kapodistrian University of Athens, Athens, Greece. Electronic address:

The aim of this study is to explore the role of labial minor salivary gland (LMSG) focus score (FS) in stratifying Sjögren's Syndrome (SS) patients, lymphoma development prediction and to facilitate early lymphoma diagnosis. Ιn an integrated cohort of 1997 patients, 618 patients with FS ≥ 1 and at least one-year elapsing time interval from SS diagnosis to lymphoma diagnosis or last follow up were identified. Clinical, laboratory and serological features were recorded. A data driven logistic regression model was applied to identify independent lymphoma associated risk factors. Furthermore, a FS threshold maximizing the difference of time interval from SS until lymphoma diagnosis between high and low FS lymphoma subgroups was investigated, to develop a follow up strategy for early lymphoma diagnosis. Of the 618 patients, 560 were non-lymphoma SS patients while the other 58 had SS and lymphoma. FS, cryoglobulinemia and salivary gland enlargement (SGE) were proven to be independent lymphoma associated risk factors. Lymphoma patients with FS ≥ 4 had a statistically significant shorter time interval from SS to lymphoma diagnosis, compared to those with FS < 4 (4 vs 9 years, respectively, p = 0,008). SS patients with FS ≥ 4 had more frequently B cell originated manifestations and lymphoma, while in patients with FS < 4, autoimmune thyroiditis was more prevalent. In the latter group SGE was the only lymphoma independent risk factor. A second LMSG biopsy is patients with a FS ≥ 4, 4 years after SS diagnosis and in those with FS < 4 and a history of SGE, at 9-years, may contribute to an early lymphoma diagnosis. Based on our results we conclude that LMSG FS, evaluated at the time of SS diagnosis, is an independent lymphoma associated risk factor and may serve as a predictive biomarker for the early diagnosis of SS-associated lymphomas.
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http://dx.doi.org/10.1016/j.jaut.2021.102648DOI Listing
July 2021

Data-driven biomarker analysis using computational omics approaches to assess neurodegenerative disease progression.

Math Biosci Eng 2021 02;18(2):1813-1832

Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, Greece.

The complexity of biological systems suggests that current definitions of molecular dysfunctions are essential distinctions of a complex phenotype. This is well seen in neurodegenerative diseases (ND), such as Alzheimer's disease (AD) and Parkinson's disease (PD), multi-factorial pathologies characterized by high heterogeneity. These challenges make it necessary to understand the effectiveness of candidate biomarkers for early diagnosis, as well as to obtain a comprehensive mapping of how selective treatment alters the progression of the disorder. A large number of computational methods have been developed to explain network-based approaches by integrating individual components for modeling a complex system. In this review, high-throughput omics methodologies are presented for the identification of potent biomarkers associated with AD and PD pathogenesis as well as for monitoring the response of dysfunctional molecular pathways incorporating multilevel clinical information. In addition, principles for efficient data analysis pipelines are being discussed that can help address current limitations during the experimental process by increasing the reproducibility of benchmarking studies.
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http://dx.doi.org/10.3934/mbe.2021094DOI Listing
February 2021

The effect of the degree and location of coronary stenosis on the hemodynamic status of a coronary vessel.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2671-2674

The ongoing advances in the field of cardiovascular modelling during the past years have allowed for the creation of accurate three-dimensional models of the major coronary arteries. The aforementioned 3D models can accurately mimic the human coronary vasculature if they are combined with sophisticated computational fluid dynamics algorithms and shed light to non-trivial issues that concern the clinicians. One of these issues is to define whether a coronary lesion is more dangerous to present with ischemia if it is at a proximal or a distal part of the vessel. In this work, we aim to investigate the aforementioned issue by reconstructing in 3D a coronary arterial model from a healthy subject using Computed Tomography Coronary Angiography images and by editing it to create eight diseased arterial models that contain one or two lesions of different severities. After carrying out the appropriate blood flow simulations using the finite element method, we observed that the distal lesions are more dangerous than the proximal ones in terms of hemodynamic significance. Moreover, the distal severe stenosis (i.e. 70% diameter reduction) present with the highest peak Wall Shear Stress (WSS) values in comparison to the proximal ones.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175302DOI Listing
July 2020

A deep learning oriented method for automated 3D reconstruction of carotid arterial trees from MR imaging.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2408-2411

The scope of this paper is to present a new carotid vessel segmentation algorithm implementing the U-net based convolutional neural network architecture. With carotid atherosclerosis being the major cause of stroke in Europe, new methods that can provide more accurate image segmentation of the carotid arterial tree and plaque tissue can help improve early diagnosis, prevention and treatment of carotid disease. Herein, we present a novel methodology combining the U-net model and morphological active contours in an iterative framework that accurately segments the carotid lumen and outer wall. The method automatically produces a 3D meshed model of the carotid bifurcation and smaller branches, using multispectral MR image series obtained from two clinical centres of the TAXINOMISIS study. As indicated by a validation study, the algorithm succeeds high accuracy (99.1% for lumen area and 92.6% for the perimeter) for lumen segmentation. The proposed algorithm will be used in the TAXINOMISIS study to obtain more accurate 3D vessel models for improved computational fluid dynamics simulations and the development of models of atherosclerotic plaque progression.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176532DOI Listing
July 2020

Predicting lymphoma outcomes and risk factors in patients with primary Sjögren's Syndrome using gradient boosting tree ensembles.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:2165-2168

Primary Sjogren's Syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction, where it has been long stated that 5% of pSS patients are prone to lymphoma development. In this work, we use clinical data from 449 pSS patients to develop a first, rule-based, supervised learning model that can be used to predict lymphoma outcomes, as well as, identify prominent features for lymphoma prediction in pSS patients. Towards this direction, the gradient boosting method combined with regression tree ensembles is used to derive a rule-based, decision model for lymphoma prediction. Our results reveal an average accuracy 87.1% and area under the curve score 88%, highlighting the importance of the C4 value, the rheumatoid factor and the lymphadenopathy factor as prominent lymphoma predictors, among others.
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http://dx.doi.org/10.1109/EMBC.2019.8857557DOI Listing
July 2019

Variation in primary Sjögren's syndrome care among European countries.

Clin Exp Rheumatol 2019 May-Jun;37 Suppl 118(3):27-28. Epub 2019 Jul 16.

Rheumatology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Italy.

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October 2019

Sjögren's syndrome towards precision medicine: the challenge of harmonisation and integration of cohorts.

Clin Exp Rheumatol 2019 May-Jun;37 Suppl 118(3):175-184. Epub 2019 Jul 3.

Pathophysiology Department, Athens School of Medicine, National and Kapodistrian University of Athens, Greece.

Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease with diverse clinical picture and outcome. The disease affects primarily middle-aged females and involves the exocrine glands leading to dry mouth and eyes. When the disease extends beyond the exocrine glands (systemic form), certain extraglandular manifestations involving liver, kidney, lungs, peripheral nervous system and the skin may occur. Primary SS is considered the crossroad between autoimmunity and lymphoproliferation, since approximately 5% of patients develop NHL associated lymphomas. As with every chronic disease with complex aetiopathogenesis and clinical heterogeneity, pSS has certain unmet needs that have to be addressed: a) classification and stratification of patients; b) understanding the distinct pathogenetic mechanisms and clinical phenotypes; c) defining and interpreting the real needs of patients regarding the contemporary diagnostic and therapeutic approaches; d) physician and patients' training regarding the wide spectrum of the disease; e) creating common policies across European countries to evaluate and manage SS patients. To achieve these goals, an intense effort is being currently undertaken by the HarmonicSS consortium in order to harmonise and integrate the largest European cohorts of pSS patients. In this review, we present an overview of our perception and vision, as well as new issues arising from this project such as harmonisation protocols and procedures, data sharing principles and various ethical and legal issues originating from these approaches.
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October 2019

Enhancing medical data quality through data curation: a case study in primary Sjögren's syndrome.

Clin Exp Rheumatol 2019 May-Jun;37 Suppl 118(3):90-96. Epub 2019 Jul 5.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, and Department of Biomedical Research, FORTH-IMBB, Ioannina, Greece.

Objectives: To address the need for automatically assessing the quality of clinical data in terms of accuracy, relevance, conformity, and completeness, through the concise development and application of an automated method which is able to automatically detect problematic fields and match clinical terms under a specific domain.

Methods: The proposed methodology involves the automated construction of three diagnostic reports that summarise valuable information regarding the types and ranges of each term in the dataset, along with the detected outliers, inconsistencies, and missing values, followed by a set of clinically relevant terms based on a reference model which serves as a set of terms which describes the domain knowledge of a disease of interest.

Results: A case study was conducted using anonymised data from 250 patients who were diagnosed with primary Sjögren's syndrome (pSS), yielding reliable outcomes that were highlighted for clinical evaluation. Our method was able to successfully identify 28 features with detected outliers, and unknown data types, as well as, identify outliers, missing values, similar terms, and inconsistencies within the dataset. The data standardisation method was able to match 76 out of 85 (89.41%) pSS-related terms according to a standard pSS reference model which has been introduced by the clinicians.

Conclusions: Our results confirm the clinical value of the data curation method towards the improvement of the dataset quality through the precise identification of outliers, missing values, inconsistencies, and similar terms, as well as, through the automated detection of pSS-related relevant terms towards data standardisation.
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October 2019

Medical data quality assessment: On the development of an automated framework for medical data curation.

Comput Biol Med 2019 04 7;107:270-283. Epub 2019 Mar 7.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, GR45110, Greece; Department of Biomedical Research, FORTH-IMBB, Ioannina, GR45110, Greece. Electronic address:

Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.
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http://dx.doi.org/10.1016/j.compbiomed.2019.03.001DOI Listing
April 2019

Towards the Establishment of a Biomedical Ontology for the Primary Sjögren's Syndrome.

Annu Int Conf IEEE Eng Med Biol Soc 2018 Jul;2018:4089-4092

Primary Sjögren's Syndrome (pSS) has been characterized as a hypersensitivity reaction type II systemic autoimmune chronic disease causing exocrine gland dysfunction mainly affecting women near the menopausal age. pSS patients exhibit dryness of the main mucosal surfaces and are highly prone to lymphoma development. This paper presents a first biomedical ontology for pSS based on a reference model which was determined by pSS clinical experts. The ensuing ontology constitutes the fundamental basis for mapping pSS-related ontologies from international cohorts to a common ontology. The ontology mapping (i.e., schematic interlinking) procedure is, in fact, a preliminary step to harmonize heterogeneous medical data obtained from various cohorts.
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http://dx.doi.org/10.1109/EMBC.2018.8513349DOI Listing
July 2018

Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve.

Eur Radiol 2019 Apr 15;29(4):2117-2126. Epub 2018 Oct 15.

Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.

Objectives: Application of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR).

Methods And Results: In 63 patients with chest pain symptoms and intermediate (20-90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland-Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88).

Conclusions: vFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions.

Key Points: Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets. In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR. vFAI may efficiently distinguish between functionally significant from non-significant lesions.
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http://dx.doi.org/10.1007/s00330-018-5781-8DOI Listing
April 2019

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective.

IEEE Rev Biomed Eng 2019 11;12:303-318. Epub 2018 Jul 11.

In this review, the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources paves the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic diseases, as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features, and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools supports the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues that arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed toward a roadmap in cohort harmonization for clinical medicine.
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http://dx.doi.org/10.1109/RBME.2018.2855055DOI Listing
July 2019

Virtual Functional Assessment of Coronary Stenoses Using Intravascular Ultrasound Imaging: A Proof-of-Concept Pilot Study.

Heart Lung Circ 2019 Apr 2;28(4):e33-e36. Epub 2018 Mar 2.

2nd Department of Cardiology, Medical School, University of Ioannina, Ioannina, Greece. Electronic address:

Aims: We aimed to investigate the performance of virtual functional assessment of coronary stenoses using intravascular ultrasound (IVUS)-based three-dimensional (3D) coronary artery reconstruction against the invasively measured fractional flow reserve (FFR).

Methods And Results: Twenty-two (22) patients with either typical symptoms of stable angina or a positive stress test, who underwent IVUS and FFR, were included in this study. Five (5) patients presented FFR values lower than the 0.80 threshold, indicating ischaemia. IVUS-based 3D reconstruction and blood flow simulation were performed and the virtual functional assessment index (vFAI) was calculated. A strong correlation between IVUS-based vFAI and FFR was observed (Spearman correlation coefficient [r]=0.88, p<0.0001). There was a small overestimation of the FFR by the IVUS-based vFAI (mean difference=0.0196±0.037; p=0.023 for difference from zero). All cases with haemodynamically significant stenoses (FFR≤0.8) were correctly categorised by the IVUS-based vFAI (vFAI≤0.8).

Conclusion: The proposed approach allows the complete and comprehensive assessment of coronary stenoses providing anatomic and physiologic information, pre- and post-intervention, using only an IVUS catheter without the use of a pressure wire.
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http://dx.doi.org/10.1016/j.hlc.2018.02.011DOI Listing
April 2019

Computational estimation of the hemodynamic significance of coronary stenoses in arterial branches deriving from CCTA: A proof-of-concept study.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:1348-1351

The development of non-invasive methods for the accurate hemodynamic assessment of the coronary vasculature has become a non-trivial matter for the everyday clinical practice. Virtual Functional Assessment Index has already been suggested as a valid alternative to the invasively measured FFR but only on coronary arterial segments. In this work, we propose a novel method for the estimation of the severity of coronary lesions in arterial branches from CCTA derived images. Four left arterial branches were reconstructed in 3D using our in-house developed 3D reconstruction algorithm, and were subjected to computational blood flow simulations for the final calculation of the vFAI through the whole arterial branch. Strong correlation was found (r=0.82) between the two methods. A small relative error of 3.2% and a small trend of overestimation (0.023, SD=0.088) were also observed. All pathological cases presenting ischemia, were correctly discriminated by our method as hemodynamically significant lesions.
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http://dx.doi.org/10.1109/EMBC.2017.8037082DOI Listing
July 2017

A novel hybrid approach for reconstruction of coronary bifurcations using angiography and OCT.

Annu Int Conf IEEE Eng Med Biol Soc 2017 Jul;2017:588-591

The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary bifurcations using biplane Coronary Angiographies and Optical Coherence Tomography (OCT) imaging. The method is based on a five step approach by improving a previous validated work in order to reconstruct coronary arterial bifurcations. In the first step the lumen borders are detected on the Frequency Domain (FD) OCT images. In the second step a semi-automated method is implemented on two angiographies for the extraction of the 2D bifurcation coronary artery centerline. In the third step the 3D path of the bifurcation artery is extracted based on a back projection algorithm. In the fourth step the lumen borders are placed onto the 3D catheter path. Finally, in the fifth step the intersection of the main and side branches produces the reconstructed model of the coronary bifurcation artery. Data from three patients are acquired for the validation of the proposed methodology and the results are compared against a reconstruction method using quantitative coronary angiography (QCA). The comparison between the two methods is achieved using morphological measures of the vessels as well as comparison of the wall shear stress (WSS) mean values.
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http://dx.doi.org/10.1109/EMBC.2017.8036893DOI Listing
July 2017

Prediction of Atherosclerotic Plaque Development in an In Vivo Coronary Arterial Segment Based on a Multilevel Modeling Approach.

IEEE Trans Biomed Eng 2017 08 19;64(8):1721-1730. Epub 2016 Oct 19.

Objective: The aim of this study is to explore major mechanisms of atherosclerotic plaque growth, presenting a proof-of-concept numerical model.

Methods: To this aim, a human reconstructed left circumflex coronary artery is utilized for a multilevel modeling approach. More specifically, the first level consists of the modeling of blood flow and endothelial shear stress (ESS) computation. The second level includes the modeling of low-density lipoprotein (LDL) and high-density lipoprotein and monocytes transport through the endothelial membrane to vessel wall. The third level comprises of the modeling of LDL oxidation, macrophages differentiation, and foam cells formation. All modeling levels integrate experimental findings to describe the major mechanisms that occur in the arterial physiology. In order to validate the proposed approach, we utilize a patient specific scenario by comparing the baseline computational results with the changes in arterial wall thickness, lumen diameter, and plaque components using follow-up data.

Results: The results of this model show that ESS and LDL concentration have a good correlation with the changes in plaque area [R = 0.365 (P = 0.029, adjusted R = 0.307) and R = 0.368 (P = 0.015, adjusted R = 0.342), respectively], whereas the introduction of the variables of oxidized LDL, macrophages, and foam cells as independent predictors improves the accuracy in predicting regions potential for atherosclerotic plaque development [R = 0.847 (P = 0.009, adjusted R = 0.738)].

Conclusion: Advanced computational models can be used to increase the accuracy to predict regions which are prone to plaque development.

Significance: Atherosclerosis is one of leading causes of death worldwide. For this purpose computational models have to be implemented to predict disease progression.
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http://dx.doi.org/10.1109/TBME.2016.2619489DOI Listing
August 2017

Prediction of time dependent survival in HF patients after VAD implantation using pre- and post-operative data.

Comput Biol Med 2016 Mar 12;70:99-105. Epub 2016 Jan 12.

Unit of Medical Technology and Intelligent Information Systems, Dept of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece. Electronic address:

Heart failure is one of the most common diseases worldwide. In recent years, Ventricular Assist Devices (VADs) have become a valuable option for patients with advanced HF. Although it has been shown that VADs improve patient survival rates, several complications persist during left VAD (LVAD) support. The stratification scores currently employed are mostly generic, i.e. not specifically built for LVAD patients, and are based on pre-implantation patient data. In this work we apply data mining approaches for the prediction of time dependent survival in patients after LVAD implantation. Moreover, the predictions acquired with the use of pre-implantation data are enriched by employing post-implantation data, i.e. follow-up data. Different clinical scenarios have been depicted and the subsequent conditions are tested in order to identify the optimal set of pre- and post-implant features, as well as the most suitable algorithms for feature selection and prediction. The proposed approach is applied to a real dataset of 71 patients, reporting an accuracy of 84.5%, sensitivity of 87% and specificity of 82%. Based on the reported results, expert cardio-surgeons can be supported in planning the treatment of VAD patients.
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http://dx.doi.org/10.1016/j.compbiomed.2016.01.005DOI Listing
March 2016

Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography--comparison and registration with IVUS.

BMC Med Imaging 2016 Jan 19;16. Epub 2016 Jan 19.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110, Ioannina, Greece.

Background: The aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA).

Methods: The methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel's centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction.

Results: The methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively.

Conclusions: The results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena.
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http://dx.doi.org/10.1186/s12880-016-0111-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4719213PMC
January 2016

Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography - comparison and registration using IVUS.

Annu Int Conf IEEE Eng Med Biol Soc 2015 Aug;2015:5638-41

The aim of this study is to present a new method for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography (CT) Angiography. The method is summarized in three steps. In the first step, image filters are applied to CT images and an initial estimation of the vessel borders is extracted. In the second step, the 3D centerline is extracted using the center of gravity of each rough artery border. Finally in the third step, the borders and the plaque are detected and placed onto the 3D centerline constructing a 3D surface. By using as gold standard the results of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method, high correlation is observed for calcium objects detected by CT and IVUS. The correlation coefficients for objects' volume, surface area, length and angle are r=0.51, r=0.89, r=0.96 and r=0.93, respectively.
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http://dx.doi.org/10.1109/EMBC.2015.7319671DOI Listing
August 2015

Validation study of a 3D-QCA coronary reconstruction method using a hybrid intravascular ultrasound and angiography reconstruction method and patient-specific Fractional Flow Reserve data.

Annu Int Conf IEEE Eng Med Biol Soc 2015 Aug;2015:973-6

The estimation of the severity of coronary lesions is of utmost importance in today's clinical practice, since Cardiovascular diseases often have fatal consequences. The most efficient method to estimate the severity of a lesion is the calculation of the Fractional Flow Reserve. The necessary use of a pressure wire, however, makes this method invasive and strenuous for the patient. In this work, we present a novel 3-Dimensional Quantitative Coronary Analysis coronary reconstruction method and a framework for the computation of the virtual Functional Assessment Index (vFAI). In a dataset of 5 coronary arterial segments, we use the aforementioned method to reconstruct them in 3D, and compare them to the respective 3D models reconstructed from our already validated hybrid IVUS-angiography reconstruction method [2]. The obtained results indicate a high correlation between the two methods in terms of the calculated FFR values, presenting a difference of 3.19% in the worst case scenario. Furthermore, when compared to the actual FFR values that derive from a pressure wire, the differences were statistically insignificant.
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http://dx.doi.org/10.1109/EMBC.2015.7318526DOI Listing
August 2015

Error propagation in the characterization of atheromatic plaque types based on imaging.

Comput Methods Programs Biomed 2015 Oct 2;121(3):161-74. Epub 2015 Jul 2.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece. Electronic address:

Imaging systems transmit and acquire signals and are subject to errors including: error sources, signal variations or possible calibration errors. These errors are included in all imaging systems for atherosclerosis and are propagated to methodologies implemented for the segmentation and characterization of atherosclerotic plaque. In this paper, we present a study for the propagation of imaging errors and image segmentation errors in plaque characterization methods applied to 2D vascular images. More specifically, the maximum error that can be propagated to the plaque characterization results is estimated, assuming worst-case scenarios. The proposed error propagation methodology is validated using methods applied to real datasets, obtained from intravascular imaging (IVUS) and optical coherence tomography (OCT) for coronary arteries, and magnetic resonance imaging (MRI) for carotid arteries. The plaque characterization methods have recently been presented in the literature and are able to detect the vessel borders, and characterize the atherosclerotic plaque types. Although, these methods have been extensively validated using as gold standard expert annotations, by applying the proposed error propagation methodology a more realistic validation is performed taking into account the effect of the border detection algorithms error and the image formation error into the final results. The Pearson's coefficient of the detected plaques has changed significantly when the method was applied to IVUS and OCT, while there was not any variation when the method was applied to MRI data.
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http://dx.doi.org/10.1016/j.cmpb.2015.06.002DOI Listing
October 2015

Patient-specific simulation of coronary artery pressure measurements: an in vivo three-dimensional validation study in humans.

Biomed Res Int 2015 1;2015:628416. Epub 2015 Mar 1.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science, University of Ioannina, 45110 Ioannina, Greece ; Michailideion Cardiac Center, University of Ioannina, 45110 Ioannina, Greece ; Biomedical Research Institute-FORTH, University of Ioannina, 45110 Ioannina, Greece.

Pressure measurements using finite element computations without the need of a wire could be valuable in clinical practice. Our aim was to compare the computed distal coronary pressure values with the measured values using a pressure wire, while testing the effect of different boundary conditions for the simulation. Eight coronary arteries (lumen and outer vessel wall) from six patients were reconstructed in three-dimensional (3D) space using intravascular ultrasound and biplane angiographic images. Pressure values at the distal and proximal end of the vessel and flow velocity values at the distal end were acquired with the use of a combo pressure-flow wire. The 3D lumen and wall models were discretized into finite elements; fluid structure interaction (FSI) and rigid wall simulations were performed for one cardiac cycle both with pulsatile and steady flow in separate simulations. The results showed a high correlation between the measured and the computed coronary pressure values (coefficient of determination [r(2)] ranging between 0.8902 and 0.9961), while the less demanding simulations using steady flow and rigid walls resulted in very small relative error. Our study demonstrates that computational assessment of coronary pressure is feasible and seems to be accurate compared to the wire-based measurements.
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http://dx.doi.org/10.1155/2015/628416DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4359837PMC
December 2015

Computerized methodology for micro-CT and histological data inflation using an IVUS based translation map.

Comput Biol Med 2015 Oct 6;65:168-76. Epub 2015 Mar 6.

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, PO Box 1186, GR 45110 Ioannina, Greece; FORTH-Institute of Molecular Biology and Biotechnology, Department of Biomedical Research, GR 45110 Ioannina, Greece; Michaelidion Cardiac Center and Dept. of Cardiology, Medical School, University of Ioannina, GR 45110 Ioannina, Greece. Electronic address:

A framework for the inflation of micro-CT and histology data using intravascular ultrasound (IVUS) images, is presented. The proposed methodology consists of three steps. In the first step the micro-CT/histological images are manually co-registered with IVUS by experts using fiducial points as landmarks. In the second step the lumen of both the micro-CT/histological images and IVUS images are automatically segmented. Finally, in the third step the micro-CT/histological images are inflated by applying a transformation method on each image. The transformation method is based on the IVUS and micro-CT/histological contour difference. In order to validate the proposed image inflation methodology, plaque areas in the inflated micro-CT and histological images are compared with the ones in the IVUS images. The proposed methodology for inflating micro-CT/histological images increases the sensitivity of plaque area matching between the inflated and the IVUS images (7% and 22% in histological and micro-CT images, respectively).
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http://dx.doi.org/10.1016/j.compbiomed.2015.02.018DOI Listing
October 2015

Machine learning applications in cancer prognosis and prediction.

Comput Struct Biotechnol J 2015 15;13:8-17. Epub 2014 Nov 15.

Unit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece ; IMBB - FORTH, Dept. of Biomedical Research, Ioannina, Greece.

Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.
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http://dx.doi.org/10.1016/j.csbj.2014.11.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4348437PMC
March 2015

Assessing the hemodynamic influence between multiple lesions in a realistic right coronary artery segment: A computational study.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:5643-6

Coronary artery disease is the primary cause of morbidity and mortality worldwide. Therefore, detailed assessment of lesions in the coronary vasculature is critical in current clinical practice. Fractional flow reserve (FFR) has been proven as an efficient method for assessing the hemodynamic severity of a coronary stenosis. However, functional assessment of a coronary segment with multiple stenoses (≥ 2) remains complex for guiding the strategy of percutaneous coronary intervention due to the hemodynamic interplay between adjacent stenoses. In this work, we created four 3-dimensional (3D) arterial models that derive from a healthy patient-specific right coronary artery segment. The initial healthy model was reconstructed using fusion of intravascular ultrasound (IVUS) and biplane angiographic patient data. The healthy 3D model presented a measured FFR value of 0.96 (pressure-wire) and a simulated FFR value of 0.98. We then created diseased models with two artificial sequential stenoses of 90% lumen area reduction or with the proximal and distal stenosis separately. We calculated the FFR value for each case: 0.65 for the case with the two stenoses, 0.73 for the case with the distal stenosis and 0.90 for the case with the proximal stenosis. This leads to the conclusion that although both stenoses had the same degree of lumen area stenosis, there was a large difference in hemodynamic severity, thereby indicating that angiographic lumen assessment by itself is often not adequate for accurate assessment of coronary lesions.
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http://dx.doi.org/10.1109/EMBC.2014.6944907DOI Listing
September 2015

A dynamic Bayesian network approach for time-specific survival probability prediction in patients after ventricular assist device implantation.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:3172-5

In this work we present a decision support tool for the calculation of time-dependent survival probability for patients after ventricular assist device implantation. Two different models have been developed, a short term one which predicts survival for the first three months and a long term one that predicts survival for one year after implantation. In order to model the time dependencies between the different time slices of the problem, a dynamic Bayesian network (DBN) approach has been employed. DBNs order to capture the temporal events of the patient disease and the temporal data availability. High accuracy results have been reported for both models. The short and long term DBNs reached an accuracy of 96.97% and 93.55% respectively.
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http://dx.doi.org/10.1109/EMBC.2014.6944296DOI Listing
October 2015

Methodology for micro-CT data inflation using intravascular ultrasound images.

Annu Int Conf IEEE Eng Med Biol Soc 2014 ;2014:1099-102

In this paper, a framework for the inflation of micro-CT data using intravascular ultrasound (IVUS) images, is presented. The proposed methodology consists of four steps. In the first step a centerline is extracted from the micro-CT images. In the second step the micro CT images are segmented automatically using the k-means algorithm. In the third step IVUS- micro-CT images are co-registered based on fiducial markers selected manually by the experts. Finally, the images are inflated by applying a transformation method on each image. The transformation method is based on the IVUS and micro-CT contour difference. The proposed methodology for inflating micro-CT images could increase the reliability of correct plaque labeling process as well to enhance the accuracy of the produced training dataset from the micro-CT images.
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http://dx.doi.org/10.1109/EMBC.2014.6943786DOI Listing
October 2016

Management and modeling of balance disorders using decision support systems: the EMBALANCE project.

Adv Exp Med Biol 2015 ;820:61-7

Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece,

In this work, we present the concept, the methodological ideas and the architecture of the EMBALANCE platform. EMBALANCE platform extends existing but generic and currently uncoupled balance modeling activities, leading to a multi-scale and patient-specific balance Hypermodel, which is incorporated to a Decision Support System (DSS), towards the early diagnosis, prediction and the efficient treatment planning of balance disorders. Various data feed the intelligent system increasing the dimensionality and personalization of the system. Human Computer Interaction techniques are utilized in order to develop the required interfaces in a user-intuitive and efficient way, while interoperable web services enhance the accessibility and acceptance of the system. The platform will be validated using both retrospective as well as prospective experimental and clinical data. The final tool will be a powerful web-based platform provided to primary and secondary care physicians across specialties, levels of training and geographical boundaries, targeting wider clinical acceptance as well as the increased confidence in the developed DSS towards the early diagnostic evaluation, behaviour prediction and effective management planning of balance problems. Currently we focus and present the management and modeling of the balance disorders.
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http://dx.doi.org/10.1007/978-3-319-09012-2_4DOI Listing
April 2015

Sequence patterns mediating functions of disordered proteins.

Adv Exp Med Biol 2015 ;820:49-59

Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece,

Disordered proteins lack specific 3D structure in their native state and have been implicated with numerous cellular functions as well as with the induction of severe diseases, e.g., cardiovascular and neurodegenerative diseases as well as diabetes. Due to their conformational flexibility they are often found to interact with a multitude of protein molecules; this one-to-many interaction which is vital for their versatile functioning involves short consensus protein sequences, which are normally detected using slow and cumbersome experimental procedures. In this work we exploit information from disorder-oriented protein interaction networks focused specifically on humans, in order to assemble, by means of overrepresentation, a set of sequence patterns that mediate the functioning of disordered proteins; hence, we are able to identify how a single protein achieves such functional promiscuity. Next, we study the sequential characteristics of the extracted patterns, which exhibit a striking preference towards a very limited subset of amino acids; specifically, residues leucine, glutamic acid, and serine are particularly frequent among the extracted patterns, and we also observe a nontrivial propensity towards alanine and glycine. Furthermore, based on the extracted patterns we set off to infer potential functional implications in order to verify our findings and potentially further extrapolate our knowledge regarding the functioning of disordered proteins. We observe that the extracted patterns are primarily involved with regulation, binding and posttranslational modifications, which constitute the most prominent functions of disordered proteins.
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http://dx.doi.org/10.1007/978-3-319-09012-2_3DOI Listing
April 2015

Assessment of optimized Markov models in protein fold classification.

J Bioinform Comput Biol 2014 Aug 14;12(4):1450016. Epub 2014 Jul 14.

Department of Materials Science and Engineering, Unit of Medical Technology and Intelligent Information Systems, University of Ioannina, GR 45110 Ioannina, Greece.

Protein fold classification is a challenging task strongly associated with the determination of proteins' structure. In this work, we tested an optimization strategy on a Markov chain and a recently introduced Hidden Markov Model (HMM) with reduced state-space topology. The proteins with unknown structure were scored against both these models. Then the derived scores were optimized following a local optimization method. The Protein Data Bank (PDB) and the annotation of the Structural Classification of Proteins (SCOP) database were used for the evaluation of the proposed methodology. The results demonstrated that the fold classification accuracy of the optimized HMM was substantially higher compared to that of the Markov chain or the reduced state-space HMM approaches. The proposed methodology achieved an accuracy of 41.4% on fold classification, while Sequence Alignment and Modeling (SAM), which was used for comparison, reached an accuracy of 38%.
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http://dx.doi.org/10.1142/S0219720014500164DOI Listing
August 2014