Results, NCT02777996.

No abstract

To objectively reappraise the role of the chest radiograph (CXR) in the clinical assessment of emphysema, we compared a standardized reading of CXR with both a visual scoring and a quantitative analysis of high resolution computed tomography (HRCT) of the chest in 46 consecutive patients with chronic obstructive pulmonary disease (COPD) and fixed expiratory airflow limitation. CXR were scored for signs of overinflation and pulmonary vascular deficiency by three independent observers. HRCT scans were independently scored for extent of emphysema and for both severity and extent of emphysema. In 28 of 46 patients, inspiratory and expiratory HRCT scans were analyzed quantitatively by measuring the mean CT number in Hounsfield Units (HU) and the percentage of lung area with CT numbers < -900 HU. Quantitative CT data were compared with reference values obtained in seven normal nonsmokers. The CXR score of emphysema showed a highly significant interobserver reproducibility and correlated linearly (p < 0.001) with HRCT visual scores and quantitative data from both inspiratory and expiratory CT scan. CXR score correlated with functional indices of airflow obstruction, overinflation, and impaired lung diffusing capacity in a way comparable to that obtained by using qualitative and quantitative CT data. Patients with no signs of emphysema on CXR had mean expiratory CT numbers within normal range and a fraction of lung area with CT numbers < -900 HU on expiratory scan not exceeding 15% of total cross-sectional area. The latter value was consistently greater than 15% in patients with CXR score > 0.(ABSTRACT TRUNCATED AT 250 WORDS)

CHD risk in acromegalic patients, predicted by FS as in nonacromegalic subjects, is low; AS might have adjunctive role only in a subset of patients. However, most patients have systemic complications of acromegaly, which participate in the assessment of global CHD risk.

Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.