Abstract:The Cyprus Study is a prospective cohort study of cardiovascular disease (CVD). Its aim is to determine the relationship of intima-media thickness (IMT) of the common carotid (IMTcc), maximum thickness of IMT in the carotid bifurcation (IMTmax), number of carotid and femoral bifurcations with plaque and total plaque thickness (TPT) (sum of the maximum plaque measurements taken from the four bifurcations scanned) with the prevalence of clinical CVD. A total of 767 individuals (46% male) over the age of 40 years were recruited from a mountain village and a town outside the capital Nicosia. In addition to clinical examination, carotid and common femoral bifurcations were scanned with ultrasound. After controlling for conventional risk factors, there was little evidence of an association of IMTcc with CVD prevalence. However, IMTmax and TPT were associated with 2.9-fold (1.22 to 7.07) and 6.87-fold (2.42 to 19.43) increased odds of CVD prevalence, respectively. In conclusion, the TPT and number of bifurcations with plaque are more strongly associated with the prevalence of CVD. These findings warrant investigation in prospective studies to document associations with incident CVD events.
DNA analysis is the gold standard for the diagnosis of ADPKD-2, especially in young people. Ultrasound diagnosis is highly dependent on age. Under the age of 14, ultrasound is not recommended as a routine diagnostic procedure, but ultrasound becomes 100% reliable in excluding ADPKD-2 in family members at 50% risk, over the age of 30. ADPKD-2 represents a mild variant of polycystic kidney disease with a low prevalence of symptoms and a late onset of end-stage renal failure.
Our aim was to explore the possibility of delineation of the facial nerve within the parotid gland and to differentiate between superficial and deep parotid lesions in relationship to it, using ultrasound, CT, MRI, MRI sialography (MRIS) and CT sialography (CTS). We examined 47 patients with clinically suspected parotid tumours by US, 31 of them also by CT, MRI and CTS, and 13 by MRIS as well. Low-intensity curvilinear structures seen on T1-weighted MRI were delineated better after intraductal gadolinium injection and proved to represent parotid ducts on CTS. Using the main parotid duct as a landmark, we distinguished parotid lesions as deep or superficial to the facial nerve by T1-weighted MRI images in 69% and by MRIS in all cases. The facial nerve itself was indistinguishable from the parotid gland in all our imaging methods.
US, CT and MR imaging followed by surgery performed by an experienced surgeon provided good clinical results in 39 patients with primary hyperparathyroidism. Preoperative localization was especially useful in patients with primary parathyroid hyperplasia or multiple adenomas and in patients with ectopic parathyroid adenomas in the root of the neck. We recommend identification of all abnormal parathyroid glands prior to surgery.
Breasts are composed of a mixture of fibrous and glandular tissue as well as adipose tissue and breast density describes the prevalence of fibroglandular tissue as it appears on a mammogram. Over the past few years, evaluation and reporting of breast density as it appears on mammograms has received a lot of attention because it impacts one's risk of developing breast cancer but also the capability of detecting breast cancer on mammograms. In addition, mammography fails in the identification of breast cancer in almost half of the women with dense breasts. Different image analysis methods have been investigated for automatic breast density classification. The presented method investigates the use of Amplitude-Modulation Frequency-Modulation (AM-FM) multi-scale feature sets for characterization of breast density as the first step in the development of a density specific Computer Aided Detection System. AM-FM decompositions use different scales and bandpass filters to extract the instantaneous frequencies (IF), instantaneous amplitude (IA) and instantaneous phase (IP) components from an image. Normalized histograms of the maximum IA across all frequencies and scales are used to model the different breast density classes. Classification of a new mammogram into one of the breast density classes is achieved using the k-nearest neighbor method with k = 5 and the euclidean distance metric. The method is evaluated on the Medical Image Analysis Society (MIAS) mammographic database and the results are presented. The presented method allows breast density classification accuracy reaching over 84%. Future work will involve a new AM-FM methodology approach based on adaptive filterbank design and performance index decision.
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