Status menopauzalny – główny czynnik determinujący dokładność prognostyczną modeli diagnostyki różnicowej guzów przydatków

Objectives: The aim of this study was to externally validate the diagnostic performance of the International Ovarian Tumor Analysis logistic regression models (LR1 and LR2, 2005) and other popular prognostic models including the Timmerman logistic regression model (1999), the Alcazar model (2003), the risk of malignancy index (RMI, 1990), and the risk of malignancy algorithm (ROMA, 2009). We compared these models to subjective ultrasonographic assessment performed by an experienced ultrasonography specialist, and with our previously developed scales: the sonomorphologic index and the vascularization index. Furthermore, we evaluated diagnostic tests with regard to the menopausal status of patients. Materials and methods: This study included 268 patients with adnexal masses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the nalyzed models. Materials and methods: This study included 268 patients with adnexal asses; 167 patients with benign ovarian tumors and 101 patients with malignant ovarian tumors were enrolled. All tumors were evaluated by using transvaginal ultrasonography according to the diagnostic criteria of the analyzed models. Results: The subjective ultrasonographic sessment and all of the studied predictive models achieved similar diagnostic performance in the whole study population. However, significant differences were observed when preand postmenopausal patients were analyzed separately. In the subgroup of premenopausal atients, the highest area under the curve (AUC) was achieved by subjective ultrasonographic assessment (0.931), the Alcazar model (0.912), and LR1 (0.909). Alternatively, in the group of postmenopausal patients, the highest AUC was noted for the Timmerman model (0.973), ROMA (0.951), and RMI (0.938). Conclusions: Menopausal status is a key factor that affects the utility of prognostic models for differential diagnosis of ovarian tumors. Diagnostic models of ovarian tumors are reasonable tools for predicting tumor malignancy.Cel: Celem pracy była zewnętrzna walidacja wybranych modeli prognostycznych: autorstwa grupy International Ovarian Tumor Analysis opartych na regresji logistycznej (LR1 i LR2, 2005) oraz innych popularnych modeli przeznaczonych do diagnostyki różnicowej guzów jajnika takich jak: model zaproponowany przez Timmerman’a i wsp. (1999), Alcazar’a i wsp., (2003), indeks ryzyka nowotworu (RMI – risk of malignancy index, 1990) oraz testu ROMA (risk of malignancy algorithm, 2009). Modele zostały porównane z subiektywną oceną ultrasonograficzną rzeprowadzoną przez doświadczonego specjalistę oraz skalami diagnostycznymi utworzonymi w naszym ośrodku: indeksem sonomorfologicznym (SM, 2004) i indeksem waskularyzacji (SD, 2004). Użyteczność analizowanych modeli została oceniona w zależności od różnych cech kliniczno-patologicznych, między innymi w zależności od statusu menopauzalnego pacjentki. Metodyka: W badaniu poddano analizie 268 guzów przydatków, w tym 167 guzów niezłośliwych i 101 nowotworów złośliwych jajnika. Każdy z guzów został oceniony w odniesieniu do kryteriów diagnostycznych analizowanych testów. Przed operacją oznaczono również poziom markerów CA125 i HE4. Wyniki: W całej badanej populacji wszystkie modele predykcyjne wykazały podobną wartość diagnostyczną. Natomiast, stwierdzono istotne różnice pomiędzy testami w sytuacji gdy analizowano osobno pacjentki przed i po menopauzie. Największe pole pod krzywą ROC (AU-ROC - area under the ROC curve) w grupie pacjentek przed menopauzą uzyskały: subiektywna ocena ultrasonograficzna (0,931), model Alcazar’a (0,912) oraz LR1 (0,909). Natomiast w grupie kobiet po menopauzie największy AU-ROC uzyskały: model Timmerman’a (0,973), ROMA (0,951) i RMI (0,938). Wnioski : Status menopauzalny jest podstawowym czynnikiem determinującym użyteczność modelu predykcyjnego w diagnostyce różnicowej guzów przydatków. Wszystkie z badanych modeli uzyskały wartość diagnostyczną umożliwiającą stosunkowo dokładną diagnostykę przedoperacyjną guzów przydatków

Vaguely defined objects: representations, fuzzy sets and nonclassical cardinality theory

In recent years, an impetuous development of new, unconventional theories, methods, techniques and technologies in computer and information sciences, systems analysis, decision-making and control, expert systems, data modelling, engineering, etc. , resulted in a considerable increase of interest in adequate mathematical description and analysis of objects, phenomena, and processes which are vague or imprecise by their very nature. Classical two-valued logic and the related notion of a set, together with its mathematical consequences, are then often inadequate or insufficient formal tools, and can even become useless for applications because of their (too) categorical character: 'true - false', 'belongs - does not belong', 'is - is not', 'black - white', '0 - 1', etc. This is why one replaces classical logic by various types of many-valued logics and, on the other hand, more general notions are introduced instead of or beside that of a set. Let us mention, for instance, fuzzy sets and derivative concepts, flou sets and twofold fuzzy sets, which have been created for different purposes as well as using distinct formal and informal motivations. A kind of numerical information concerning of 'how many' elements those objects are composed seems to be one of the simplest and more important types of information about them. To get it, one needs a suitable notion of cardinality and, moreover, a possibility to calculate with such cardinalities. Unfortunately, neither fuzzy sets nor the other nonclassical concepts have been equipped with a satisfactory (nonclassical) cardinality theory

Intelligent counting under information imprecision: applications to intelligent systems and decision support

Counting belongs to the most elementary and frequent mental activities of human beings. Its results are a basis for coming to a decision in a lot of situations and dimensions of our life. This book presents a novel approach to the advanced and sophisticated case, called intelligent counting, in which the objects of counting are imprecisely, fuzzily specified. Formally, this collapses to counting in fuzzy sets, interval-valued fuzzy sets or I-fuzzy sets (Atanassov's intuitionistic fuzzy sets).   The monograph is the first one showing and emphasizing that the presented methods of intelligent counting are human-consistent: are reflections and formalizations of real, human counting procedures performed under imprecision and, possibly, incompleteness of information. Other applications of intelligent counting in various areas of intelligent systems and decision support will be discussed, too.   The whole presentation is self-contained, systematic, and equipped with many examples, figures and tables. Computer and information scientists, researchers, engineers and practitioners, applied mathematicians, and postgraduate students interested in information imprecision are the target readers

Vagueness and its representations: a unifying look

Using the notion of a vaguely defined object, we systematize and unify different existing approaches to vagueness and its mathematical representations, including fuzzy sets and derived concepts. Moreover, a new, approximative approach to vaguely defined objects will be introduced and investigated