Osteoporosis is a silent disease without any evidence of disease until a fracture occurs. Approximately 200 million people in the world are affected by osteoporosis and 8.9 million fractures occur each year worldwide. Fractures of the hip are a major public health burden, by means of both social cost and health condition of the elderly because these fractures are one of the main causes of morbidity, impairment, decreased quality of life and mortality in women and men. The aim of this review is to analyze the most important factors related to the enormous impact of osteoporotic fractures on population. Among the most common risk factors, low body mass index; history of fragility fracture, environmental risk, early menopause, smoking, lack of vitamin D, endocrine disorders (for example insulin-dependent diabetes mellitus), use of glucocorticoids, excessive alcohol intake, immobility and others represented the main clinical risk factors associated with augmented risk of fragility fracture. The increasing trend of osteoporosis is accompanied by an underutilization of the available preventive strategies and only a small number of patients at high fracture risk are recognized and successively referred for therapy. This report provides analytic evidences to assess the best practices in osteoporosis management and indications for the adoption of a correct healthcare strategy to significantly reduce the osteoporosis burden. Early diagnosis is the key to resize the impact of osteoporosis on healthcare system. In this context, attention must be focused on the identification of high fracture risk among osteoporotic patients. It is necessary to increase national awareness campaigns across countries in order to reduce the osteoporotic fractures incidence.

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Accurate knowledge of the liver structure, including liver surface and lesion localization, is usually required in treatments such as liver tumor ablations and/or radiotherapy. This paper presents a new method and corresponding algorithm for fast segmentation of the liver and its internal lesions from CT scans. No interaction between the user and analysis system is required for initialization since the algorithm is fully automatic. A statistical model-based approach was created to distinguish hepatic tissue from other abdominal organs. It was combined to an active contour technique using gradient vector flow in order to obtain a smoother and more natural liver surface segmentation. Thereafter, automatic classification was performed to isolate hepatic lesions from liver parenchyma. Twenty-one datasets, presenting different anatomical and pathological situations, have been processed and analyzed. Special focus has been driven to the resulting processing time together with quality assessment. Our method allowed robust and efficient liver and lesion segmentations very close to the ground truth, in a relatively short processing time (average of 11.4 s for a 512 x 512-pixel slice). A volume overlap of 94.2% and an accuracy of 3.7 mm were achieved for liver surface segmentation. Sensitivity and specificity for tumor lesion detection were 82.6% and 87.5%, respectively.

Silica nanoparticles at a diameter of about 330 nm are very promising contrast agents for ultrasound imaging and specific tumor targeting at conventional diagnostic frequencies, being in particular automatically detectable with high sensitivity already at low doses. Future studies will be carried out to assess the acoustic behavior of nanoparticles with different geometries/sizes and to improve sensitivity of the automatic detection algorithm.