Εφαρμογή συστήματος εξόρυξης δεδομένων σε ιατρικά δεδομένα

101 σ.Σκοπός της διπλωματικής εργασίας είναι η ανάπτυξη ολοκληρωμένης μεθοδολογίας για την παροχή χρήσιμων στατιστικών πληροφοριών σε μαστογραφικά δεδομένα, καθώς και η δημιουργία εργαλείων πρόβλεψης καλοήθους και κακοήθους όγκου στον μαστό βάσει των διαθέσιμων ιατρικών δεδομένων. Αρχικά, συλλέχθηκαν από το διαδίκτυο και εξετάστηκαν δύο βάσεις δεδομένων μαστογραφιών από πραγματικά ιατρικά στοιχεία . Στη συνέχεια, με τη βοήθεια του σχεδιασμού και της ανάλυσης απαιτήσεων αναπτύχθηκαν και υλοποιήθηκαν οι τελικές βάσεις δεδομένων στις οποίες αποθηκεύτηκαν τα δεδομένα προς περαιτέρω επεξεργασία. Παράλληλα με το σχεδιασμό κάθε βάσης, πραγματοποιούταν η προεργασία για το σχεδιασμό και την ανάλυση απαιτήσεων των τεχνικών εξόρυξης που θα χρησιμοποιηθούν σε επόμενο . Στη συνέχεια πραγματοποιήθηκε βιβλιογραφική ανασκόπηση των όρων «εξόρυξη δεδομένων» και «καρκίνος του μαστού» καθώς και οι υπάρχουσες τεχνικές διάγνωσης του καρκίνου του μαστού μέχρι σήμερα. Στη συνέχεια επελέγησαν μετά από έρευνα οι δύο καταλληλότεροι αλγόριθμοι για την μελέτη και αξιοποίηση των δεδομένων των δύο βάσεων. Η μεθοδολογία που χρησιμοποιήθηκε είναι η εξόρυξη δεδομένων με την πλέον αποτελεσματική τεχνική: «δένδρα αποφάσεων» (decision trees), καθώς επίσης και με την τεχνική ομαδοποίησης (clustering). Η τροποποίηση των αλγορίθμων και ο πειραματισμός με τη μεθοδολογία των εν λόγω τεχνικών οδήγησε στη σύσταση και τον καθορισμό των παραμέτρων των δύο αλγορίθμων για την αποτελεσματικότερη και σωστότερη πρόβλεψη του είδους του όγκου. Τέλος, έγινε σύγκριση μεταξύ των δύο τεχνικών με τρεις διαφορετικές τεχνικές μέτρησης των αποτελεσμάτων με κύριο κριτήριο την πρόβλεψη του όγκου (καλοήθης ή κακοήθης).The purpose of this thesis is to develop a methodology of useful statistical information on mammographic data as well as a technique for early diagnosis based on the records of benign and malignant breast tumor. Initially there were created two mammographic databases of true medical data available online. An analysis of the design and the requirements of the databases for data processing storage were initially developed. Furthermore, with the design for each data base, the groundwork for the design and analysis requirements of data mining techniques that would be required later on for the project were achieved. Further study review was performed in order to define the terms “data mining” and “breast cancer” as well as the existing diagnostic techniques up to date. Then two algorithms were selected which were suitable for the study and use for the two databases. The methodology selected is the “data mining” with the most effective technic decision trees as well as the technic “clustering”. The modification of the algorithms and experiment on these techniques led to the recommendation but also to definition of the parameters of two algorithms for efficient and more accurate diagnosis of the type of tumor (benign or malignant). The two algorithmic techniques were compared with three different techniques for measuring their results, where the main criterion was their success in the diagnosis of the kind of tumor (benign or malignant).Χρήστος Σ. Αλεξιάδη

Transformation of measurement uncertainties into low-dimensional feature vector space

Advances in technology allow the acquisition of data with high spatial and temporal resolution. These datasets are usually accompanied by estimates of the measurement uncertainty, which may be spatially or temporally varying and should be taken into consideration when making decisions based on the data. At the same time, various transformations are commonly implemented to reduce the dimensionality of the datasets for postprocessing or to extract significant features. However, the corresponding uncertainty is not usually represented in the low-dimensional or feature vector space. A method is proposed that maps the measurement uncertainty into the equivalent low-dimensional space with the aid of approximate Bayesian computation, resulting in a distribution that can be used to make statistical inferences. The method involves no assumptions about the probability distribution of the measurement error and is independent of the feature extraction process as demonstrated in three examples. In the first two examples, Chebyshev polynomials were used to analyse structural displacements and soil moisture measurements; while in the third, principal component analysis was used to decompose the global ocean temperature data. The uses of the method range from supporting decision-making in model validation or confirmation, model updating or calibration and tracking changes in condition, such as the characterization of the El Niño Southern Oscillation

TLR3 expression induces apoptosis in human non‐small‐cell lung cancer

The prognostic value of Toll\u2010like receptor 3 (TLR3) is debated in cancer, differing between tumor types, methods, and cell types. We recently showed for the first time that TLR3 expression on early stage non\u2010small\u2010cell lung cancer (NSCLC) results associated with a good prognosis. Here, we provide experimental evidences explaining the molecular reason behind TLR3\u2019s favorable prognostic role. We demonstrated that TLR3 activation in vitro induces apoptosis in lung cancer cell lines and, accordingly, that TLR3 expression is associated with caspase\u20103 activation in adenocarcinoma NSCLC specimens, both evaluated by immunohistochemistry. Moreover, we showed that TLR3 expression on cancer cells contributes to activate the CD103+ lung dendritic cell subset, that is specifically associated with processing of antigens derived from apoptotic cells and their presentation to CD8+ T lymphocytes. These findings point to the relevant role of TLR3 expression on lung cancer cells and support the use of TLR3 agonists in NSCLC patients to re\u2010activate local innate immune response

Characterisation of large changes in wind power for the day-ahead market using a fuzzy logic approach

Wind power has become one of the renewable resources with a major growth in the electricity market. However, due to its inherent variability, forecasting techniques are necessary for the optimum scheduling of the electric grid, specially during ramp events. These large changes in wind power may not be captured by wind power point forecasts even with very high resolution Numerical Weather Prediction (NWP) models. In this paper, a fuzzy approach for wind power ramp characterisation is presented. The main benefit of this technique is that it avoids the binary definition of ramp event, allowing to identify changes in power out- put that can potentially turn into ramp events when the total percentage of change to be considered a ramp event is not met. To study the application of this technique, wind power forecasts were obtained and their corresponding error estimated using Genetic Programming (GP) and Quantile Regression Forests. The error distributions were incorporated into the characterisation process, which according to the results, improve significantly the ramp capture. Results are presented using colour maps, which provide a useful way to interpret the characteristics of the ramp events

Ekspresija i obrada somatostatina u gušterači u razvoju i u duktalnom adenokarcinomu gušterače

Somatostatin is a gastrointestinal peptide hormone that inhibits growth of pancreatic cancer as reported by an increasing body of evidence. Yet this is not always the case. To clarify the controversy we aimed to identify the expression of somatostatin in developing human embryonic pancreatic tissue and pancreatic adenocarcinoma given that somatostatin positive cells were shown either into primitive pancreatic ductal epithelium or into pancreatic carcinoma. Tissue sections representing pancreatic fetal specimens (n=15) and ductal pancreatic adenocarcinoma specimens (n=15) were assessed using immunohistochemical methods for somatostatin expression. Normal primitive exocrine ductal epithelium and endocrine epithelium showed a definite, statistically significant, higher expression of somatostatin over neoplastic pancreatic tissue of mixed (ductal-endocrine) and pure ductal type (p1=0.021, p2=0.001, p3<0.0001and p4=0.003 respectively) during the 8th to the 10th week. No statistically significantly different expression of somatostatin in the mantle zone of the islets over neoplastic tissue of mixed (p5=0.16) and pureductal type (p6=0.65), from the 13th to the 24th week was demonstrated. Pancreatic cancer cells can express somatostatin in a model that reproduces the normal expression of the peptide by d-cells during embryonal organogenesis. Therapy aimed at pancreatic cancer must be targeted to somatostatin and analogues as a potential adjuvant novel option.Somatostatin je probavni peptidni hormon koji suzbija rast raka gušterače, za što postoji sve više dokaza. No to se ne događa uvijek. Cilj studije bio je utvrditi ekspresiju somatostatina u ljudskom embrijskom tkivu gušterače u razvoju i u adenokarcinomu gušterače, s tim da su na somatostatin pozitivne stanice dokazane ili u primitivnom duktalnom epitelu gušterače ili u karcinomu gušterače. Tkivni isječci koji su predstavljali uzorke fetalne gušterače (n=15) i uzorke adenokarcinoma gušterače (n=15) ispitani su pomoću imunohistokemijskih metoda za ekspresiju somatostatina. Normalan primitivni egzokrini duktalni epitel i endokrini epitel pokazao je konačnu, statistički značajno višu ekspresiju somatostatina iznad neoplastičnog tkiva gušterače miješanog (duktalno-endokrinog) i čistog duktalnog tipa (p1=0,021, P2=0,001, p3<0,0001 odnosno p4=0,003) tijekom 8. do 10. tjedna. Nije dokazana statistički značajno različita ekspresija somatostatina u ovojnom sloju (mantle zone, mantle layer) otočića iznadneoplastičnog tkiva miješanog (p5=0,16) i čistog duktalnog tipa (p6=0,65) od 13. do 24. tjedna. Dakle, stanice raka gušterače mogu izražavati somatostatin na naein koji ponavlja normalnu d-staničnu ekspresiju peptida za vrijeme embrijske organogeneze. Liječenje zbog raka gušterače usmjereno na somatostatin i njegove analoge moglo bi predstavljati novu mogućnosti adjuvantne terapije

Toll-like receptor 3 as a new marker to detect high risk early stage Non-Small-Cell Lung Cancer patients

Immune and epithelial cells express TLR3, a receptor deputed to respond to microbial signals activating the immune response. The prognostic value of TLR3 in cancer is debated and no data are currently available in NSCLC, for which therapeutic approaches that target the immune system are providing encouraging results. Dissecting the lung immune microenvironment could provide new prognostic markers, especially for early stage NSCLC for which surgery is the only treatment option. In this study we investigated the expression and the prognostic value of TLR3 on both tumor and immune compartments of stage I NSCLCs. In a cohort of 194 NSCLC stage I, TLR3 immunohistochemistry expression on tumor cells predicted a favorable outcome of early stage NSCLC, whereas on the immune cells infiltrating the tumor stroma, TLR3 expression associated with a poor overall survival. Patients with TLR3-positive immune infiltrating cells, but not tumor cells showed a worse prognosis compared with all other patients. The majority of TLR3-expressing immune cells resulted to be macrophages and TLR3 expression associates with PD-1 expression. TLR3 has an opposite prognostic significance when expressed on tumor or immune cells in early stage NCSCL. Analysis of TLR3 in tumor and immune cells can help in identifying high risk stage I patients for which adjuvant treatment would be beneficial

Diffuse large B-cell lymphoma arising from a multicentric mixed variant of Castleman's disease

This case report describes a patient with multicentric mixed type Castleman\u2032s disease and concomitant non-Hodgkin\u2032s lymphoma of diffuse large B cell type in the neck. Multicentric CD is a systemic illness with disseminated lymphadenopathy; its aggressive and usually fatal course is associated with infectious complications and risk for malignant tumors, such as lymphoma or Kaposi sarcoma