ADIPONECTIN AND CARDIOVASCULAR RISK PREDICTION: STRATIFICATION OF CHEST PAIN PATIENTS BY A CLUSTER ANALYSIS

Cardiovascular disease (CVD) remains the major cause of death and there is the need to a better stratification of CVD patients. By an unbiased statistical approach we sought to identify clusters of patients to better stratify their risk. 202 patients with chest pain (63% males, age 62?12 yr) undergone to CT coronary angiography (CCTA) were prospectively included and classified using K-means cluster analysis of clinical, imaging and bio-humoral data. The most relevant classification resulted in three phenotypes distinguished according to Framingham score and HMW adiponectin plasma levels. Presence and severity of disease as assessed by CCTA were verified trough these phenotypes. By K-means cluster analysis, we identified CVD phenotypes allowing to stratify patients requiring different diagnostic and therapeutic approach

Morpho-functional imaging of coronary anatomy and left ventricular perfusion obtained by cardiac CT

Volumetric computed tomography (CT) angiography has become a standard non-invasive routine procedure for cardiac imaging and coronary arteries pathology detection. However, before the diagnosis process, a pre-processing task is critical for an accurate examination of the vessels. Specially, the user has to manually remove obscuring structures in order to get an accurate visualization of coronary arteries. Indeed, the coronaries are always hidden by surrounding organs of the heart such as liver, sternum, ribs and lungs which prevent the pathologist from getting a clear view of the heart surface. In this paper, we propose a fast algorithm to automatically isolate the heart anatomy in 3D CT cardiac data sets. Our work eliminates the tedious and time consuming step of the manual delineation and pro- vides a clear and well defined view of the coronary arteries. Consequently, the user can quickly identify suspicious segments on the isolated heart. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization [1]. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object

Epicardial fat volume assessment in cardiac CT

Epicardial fat, as other visceral fat localizations, is correlated with car- diovascular disease, cardiovascular risk factors and metabolic syndrome. However, many concerns remain about the method for measuring epi- cardial fat, its regional distribution on the myocardium, as well as the accuracy and reproducibility of such measurements. At present, dedi- cated software procedures to assess epicardial fat are lacking. On the other hand, manual fat segmentation requires a huge and tedious operator intervention, which is expected to cause inaccuracy and large observer- dependent variability. The aim of this study was twofold: (1) the devel- opment of a procedure devoted to assess the volume of epicardial fat, (2) the evaluation of the related intra and inter-observer variability in CT scans, both with and without contrast medium injection