LONG‐TERM FOLLOW UP OF PATIENTS WITH SMALL GASTROINTESTINAL STROMAL TUMORS IN THE STOMACH USING ENDOSCOPIC ULTRASONOGRAPHY‐GUIDED FINE‐NEEDLE ASPIRATION BIOPSY

Background: Gastrointestinal stromal tumors (GIST) are one of the most common mesenchymal tumors of the gastrointestinal tract. GIST are defined by positive immunohistochemical staining for KIT or CD34 and thus are generally diagnosed after surgery. Because small GIST are rarely diagnosed before surgery, the clinical course of these small tumors is not clear. The aim of the present study was to follow changes in size and configuration of small GIST that were pathologically confirmed using endoscopic ultrasonography‐guided fine‐needle aspiration biopsy (EUS‐FNAB). Methods: Between July 1997 and December 2003, 16 tumors in 16 patients (10 men and 6 women) with an immunohistochemical diagnosis of GIST were regularly followed in our hospital. The median patient age when EUS‐FNAB was performed was 62 years (range 26–82 years) and the median follow‐up period was 4.9 years (range 0.5–9.6 years). Results: Fourteen tumors showed no remarkable changes in size and shape during follow up compared with the initial diagnosis. Two tumors enlarged: one tumor approximately doubled its diameter in 8 years and the other tumor increased from 1.8 cm at diagnosis to up to 10 cm after only 2 years. Doubling time of the latter tumor was calculated as 3.1 months. Conclusions: We conclude that EUS‐FNAB might be a good modality for final diagnosis of GIST without surgery, and that GIST without rapid growth on follow up can be endoscopically followed.     

Decision trees based on automatic learning and their use in cardiology

Computerized information systems, especially decision support systems, have become an increasingly important role in medical applications, particularly in those where important decision must be made effectively and reliably. But the possibility of using computers in medical decision making is limited by many difficulties, including the complexity of conventional computer languages, methodologies and tools. Thus a conceptual simple decision making model with the possibility of automating learning should be used. In this paper we introduce a cardiological knowledge-based system based on the decision tree approach supporting the mitral valve prolapse determination. Prolapse is defined as the displacement of a bodily part from its normal position. The term mitral valve prolaps (PMV), therefore, implies that the mitral leaflets are displaced relative to some structure, generally taken to be the mitral annulus. The implications of the PMV are the following: disturbed normal laminar blood flow, turbulence of the blood flow, injury of the chordae tendinae, the possibility of thrombus's composition, bacterial endocarditis, and finally hemodynamic changes defined as mitral insufficiency and mitral regurgitation. Uncertainty persists about how it should be diagnosed and about its clinical importance. It is our deep belief that the echocardiography enables properly trained experts armed with proper criteria to evaluate PMV almost 100%. But unfortunately, there are some problems concerned with the use of echocardiography. In that manner we have decided to start a research project aimed at finding new criteria and enabling the general practitioner to evaluate PMV using conventional methods and to select potential patients from the general population. To empower one to perform needed activities we have developed a computer tool called ROSE (computeRised prOlaps Syndrom dEtermination) based on algorithms of automatic learning. This tool supports the definition of new criteria and the selection of potential PMV-patients.