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Search Results: 1 - 10 of 1321 matches for " Georgios Androutsopoulos "
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ErbB Receptors and ErbB Targeted Therapies in Endometrial Cancer  [PDF]
Georgios Androutsopoulos, Georgios Michail, Georgios Adonakis, Georgios Decavalas
Journal of Cancer Therapy (JCT) , 2014, DOI: 10.4236/jct.2014.56055
Abstract:

The Epidermal Growth Factor system is present in human organs and plays an important role in cell proliferation, differentiation and apoptosis during embryogenesis and postnatal development. It has four receptors (EGFR, ErbB-2, ErbB-3 and ErbB-4) and numerous ligands. Dysregulation of the Epidermal Growth Factor signaling network is implicated in the pathogenesis of various disorders. Especially in cancer, the Epidermal Growth Factor system becomes hyperactivated with various mechanisms (ligand overproduction, receptor overproduction, constitutive receptor activation). EGFR overexpression may have a dual role in endometrial cancer. It seems that in type I endometrial cancer, EGFR overexpression did not affect disease progression. However in type II endometrial cancer, EGFR overexpression associated with high grade disease and adverse clinical outcome. Moreover ΕrbB-2 overexpression especially in type II endometrial cancer, is an indicator of a highly aggressive disease with poor overall survival. The potential role of ErbB receptors (especially EGFR and ErbB-2) as targets for cancer therapy has been investigated for over 20 years. There are 2 major classes of ErbB targeted therapies: anti-ErbB monoclonal antibodies (MoAbs) and ErbB-specific tyrosine kinase inhibitors (TKIs). ErbB targeted therapies have still shown modest effect in unselected endometrial cancer patients. However, they may be clinically active as adjuvant therapy in well-defined subgroups of type II endometrial cancer patients with EGFR and ErbB-2 overexpression.

Systemic Sclerosis and Multiple Cancers of the Female Genital Tract: Prolonged Survival following Current Treatment Strategies
Georgios Androutsopoulos,Georgios Adonakis,Athanasios Tsamandas,Andreas Andonopoulos,Georgios Decavalas
Case Reports in Rheumatology , 2011, DOI: 10.1155/2011/392068
Abstract: Background. Systemic sclerosis is a rare, chronic, multisystem, and autoimmune disease. There is an overall increased risk of malignancy in patients with systemic sclerosis. However, multiple cancers of the female genital tract in patients with SSc are a very rare event. Our aim is to present a case of SSc and multiple cancers of the female genital tract, with prolonged survival following current treatment strategies. Case. The patient, a 43-year-old nulliparous premenopausal Greek woman suffering from systemic sclerosis, presented with a history of abdominal pain and abnormal uterine bleeding. She underwent total abdominal hysterectomy with bilateral salpingo-oophorectomy, total omentectomy, appendectomy, and pelvic lymph node dissection. The histopathology revealed synchronous primary cancers of the endometrium and left ovary. The final diagnosis was stage Ib endometrial cancer endometrioid type and stage IIIc ovarian cancer endometrioid type. She underwent postoperative adjuvant chemotherapy and remains well without evidence of disease 89 months after initial surgery. Conclusion. Although our patient was diagnosed at advanced stage disease, prolonged survival may be related with radical surgery and postoperative adjuvant chemotherapy according to current treatment strategies.
Probabilistic Cascading for Large Scale Hierarchical Classification
Aris Kosmopoulos,Georgios Paliouras,Ion Androutsopoulos
Computer Science , 2015,
Abstract: Hierarchies are frequently used for the organization of objects. Given a hierarchy of classes, two main approaches are used, to automatically classify new instances: flat classification and cascade classification. Flat classification ignores the hierarchy, while cascade classification greedily traverses the hierarchy from the root to the predicted leaf. In this paper we propose a new approach, which extends cascade classification to predict the right leaf by estimating the probability of each root-to-leaf path. We provide experimental results which indicate that, using the same classification algorithm, one can achieve better results with our approach, compared to the traditional flat and cascade classifications.
Vaginal Primary Malignant Melanoma: A Rare and Aggressive Tumor
Georgios Androutsopoulos,Emmanouil Terzakis,Georgia Ioannidou,Athanasios Tsamandas,Georgios Decavalas
Case Reports in Obstetrics and Gynecology , 2013, DOI: 10.1155/2013/137908
Abstract: Vaginal primary malignant melanoma is a rare and very aggressive tumor. It most commonly occurs in postmenopausal women, with a mean age of 57 years. Our patient is an 80-year-old, postmenopausal Greek woman presented with a complaint of abnormal vaginal bleeding. On gynecologic examination there was a pigmented, raised, ulcerated, and irregular lesion ?cm in the upper third of anterior vaginal wall. She underwent a wide local excision of the lesion. The histopathology revealed vaginal primary malignant melanoma with ulceration and no clear surgical margins. She denied any additional surgical interventions and underwent to postoperative adjuvant radiotherapy. Follow up 5 months after initial diagnosis revealed no evidence of local recurrence or distant metastasis. The prognosis of vaginal primary malignant melanoma is very poor despite treatment modality, because most of the cases are diagnosed at advanced stage. Particularly patients with no clear surgical margins and tumor size >3?cm needed postoperative adjuvant radiotherapy. 1. Introduction Vaginal primary malignant melanoma (VPMM) is a rare and very aggressive tumor [1, 2]. It accounts for 0.3–0.8% of all malignant melanomas, 2–5% of female genital tract melanomas, and less than 3% of all vaginal malignancies [1–3]. About 250 cases have been reported in the English literature [1, 2]. The estimated incidence of VPMM is 0.026/100,000 women per year [2, 3]. It most commonly occurs in postmenopausal women, with a mean age of 57 years [4–6]. There are no significant differences in VPMM incidence, between various racial or ethnic groups [3, 7]. The precise etiology of VPMM is relative unknown [8]. It is thought that VPMM arises from melanocytes present in the vaginal epithelium [8, 9]. However, it is obvious that ultraviolet radiation is not the causal factor in VPMM [7]. Our aim is to present a case of VPMM that underwent a wide local excision and postoperative adjuvant radiotherapy. 2. Case Presentation The patient, an 80-year-old, gravida 3, para 2 postmenopausal Greek woman, presented to the Department of Obstetrics and Gynecology of the University of Patras Medical School with a complaint of abnormal vaginal bleeding. Her medical history included hypertension and diabetes mellitus. She had menopause at the age of 50. Her surgical history was unremarkable. Her family history revealed no evidence of cancer among the first-degree relatives. On gynecologic examination there was a pigmented, raised, ulcerated, and irregular lesion ?cm in the upper third of anterior vaginal wall. There were no palpable
Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
Aris Kosmopoulos,Ioannis Partalas,Eric Gaussier,Georgios Paliouras,Ion Androutsopoulos
Computer Science , 2013, DOI: 10.1007/s10618-014-0382-x
Abstract: Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification is the evaluation of different classification algorithms, which is complicated by the hierarchical relations among the classes. Several evaluation measures have been proposed for hierarchical classification using the hierarchy in different ways. This paper studies the problem of evaluation in hierarchical classification by analyzing and abstracting the key components of the existing performance measures. It also proposes two alternative generic views of hierarchical evaluation and introduces two corresponding novel measures. The proposed measures, along with the state-of-the art ones, are empirically tested on three large datasets from the domain of text classification. The empirical results illustrate the undesirable behavior of existing approaches and how the proposed methods overcome most of these methods across a range of cases.
Ellogon: A New Text Engineering Platform
Georgios Petasis,Vangelis Karkaletsis,Georgios Paliouras,Ion Androutsopoulos,Constantine D. Spyropoulos
Computer Science , 2002,
Abstract: This paper presents Ellogon, a multi-lingual, cross-platform, general-purpose text engineering environment. Ellogon was designed in order to aid both researchers in natural language processing, as well as companies that produce language engineering systems for the end-user. Ellogon provides a powerful TIPSTER-based infrastructure for managing, storing and exchanging textual data, embedding and managing text processing components as well as visualising textual data and their associated linguistic information. Among its key features are full Unicode support, an extensive multi-lingual graphical user interface, its modular architecture and the reduced hardware requirements.
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
Ion Androutsopoulos,Georgios Paliouras,Vangelis Karkaletsis,Georgios Sakkis,Constantine D. Spyropoulos,Panagiotis Stamatopoulos
Computer Science , 2000,
Abstract: We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.
A Principled Framework for Constructing Natural Language Interfaces To Temporal Databases
Ion Androutsopoulos
Computer Science , 1996,
Abstract: Most existing natural language interfaces to databases (NLIDBs) were designed to be used with ``snapshot'' database systems, that provide very limited facilities for manipulating time-dependent data. Consequently, most NLIDBs also provide very limited support for the notion of time. The database community is becoming increasingly interested in _temporal_ database systems. These are intended to store and manipulate in a principled manner information not only about the present, but also about the past and future. This thesis develops a principled framework for constructing English NLIDBs for _temporal_ databases (NLITDBs), drawing on research in tense and aspect theories, temporal logics, and temporal databases. I first explore temporal linguistic phenomena that are likely to appear in English questions to NLITDBs. Drawing on existing linguistic theories of time, I formulate an account for a large number of these phenomena that is simple enough to be embodied in practical NLITDBs. Exploiting ideas from temporal logics, I then define a temporal meaning representation language, TOP, and I show how the HPSG grammar theory can be modified to incorporate the tense and aspect account of this thesis, and to map a wide range of English questions involving time to appropriate TOP expressions. Finally, I present and prove the correctness of a method to translate from TOP to TSQL2, TSQL2 being a temporal extension of the SQL-92 database language. This way, I establish a sound route from English questions involving time to a general-purpose temporal database language, that can act as a principled framework for building NLITDBs. To demonstrate that this framework is workable, I employ it to develop a prototype NLITDB, implemented using ALE and Prolog.
Temporal Meaning Representations in a Natural Language Front-End
I. Androutsopoulos
Computer Science , 1999,
Abstract: Previous work in the context of natural language querying of temporal databases has established a method to map automatically from a large subset of English time-related questions to suitable expressions of a temporal logic-like language, called TOP. An algorithm to translate from TOP to the TSQL2 temporal database language has also been defined. This paper shows how TOP expressions could be translated into a simpler logic-like language, called BOT. BOT is very close to traditional first-order predicate logic (FOPL), and hence existing methods to manipulate FOPL expressions can be exploited to interface to time-sensitive applications other than TSQL2 databases, maintaining the existing English-to-TOP mapping.
Learning to Order Facts for Discourse Planning in Natural Language Generation
Aggeliki Dimitromanolaki,Ion Androutsopoulos
Computer Science , 2003,
Abstract: This paper presents a machine learning approach to discourse planning in natural language generation. More specifically, we address the problem of learning the most natural ordering of facts in discourse plans for a specific domain. We discuss our methodology and how it was instantiated using two different machine learning algorithms. A quantitative evaluation performed in the domain of museum exhibit descriptions indicates that our approach performs significantly better than manually constructed ordering rules. Being retrainable, the resulting planners can be ported easily to other similar domains, without requiring language technology expertise.
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