The Danish randomized lung cancer CT screening trial--overall design and results of the prevalence round.
ABSTRACT Lung cancer screening with low dose computed tomography (CT) has not yet been evaluated in randomized clinical trials, although several are underway.
In The Danish Lung Cancer Screening Trial, 4104 smokers and previous smokers from 2004 to 2006 were randomized to either screening with annual low dose CT scans for 5 years or no screening. A history of cigarette smoking of at least 20 pack years was required. All participants have annual lung function tests, and questionnaires regarding health status, psychosocial consequences of screening, smoking habits, and smoking cessation. Baseline CT scans were performed in 2052 participants. Pulmonary nodules were classified according to size and morphology: (1) Nodules smaller than 5 mm and calcified (benign) nodules were tabulated, (2) Noncalcified nodules between 5 and 15 mm were rescanned after 3 months. If the nodule increased in size or was larger than 15 mm the participant was referred for diagnostic workup.
At baseline 179 persons showed noncalcified nodules larger than 5 mm, and most were rescanned after 3 months: The rate of false-positive diagnoses was 7.9%, and 17 individuals (0.8%) turned out to have lung cancer. Ten of these had stage I disease. Eleven of 17 lung cancers at baseline were treated surgically, eight of these by video assisted thoracic surgery resection.
Screening may facilitate minimal invasive treatment and can be performed with a relatively low rate of false-positive screen results compared with previous studies on lung cancer screening.
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ABSTRACT: Introduction: Recruitment and nodule management are critical issues of lung cancer screening with low-dose computed tomography (LDCT). We report subjects’ compliance and results of LDCT screening and management protocol in the active arm of the ITALUNG trial. Methods: Three thousand two hundred six smokers or former smokers invited by mail were randomized to receive four annual LDCT (n = 1613) or usual care (n = 1593). Management protocol included follow-up LDCT, 2-[18F]fluoro-2-deoxy-D glucose positron emission tomography (FDG-PET), and CT-guided fine-needle aspiration biopsy (FNAB). Results: One thousand four hundred six subjects (87%) underwent baseline LDCT, and 1263 (79%) completed four screening rounds. LDCT was positive in 30.3% of the subjects at baseline and 15.8% subsequently. Twenty-one lung tumors in 20 subjects (1.5% detection) were found at baseline, and 20 lung tumors in 18 subjects (0.5% detection) in subsequent screening rounds. Ten of 18 prevalent (55%) and 13 of 17 incident (76%) non–small-cell cancers were in stage I. Interval growth enabled diagnosis of lung cancer in 16 subjects (42%), but at least one follow-up LDCT was obtained in 741 subjects (52.7%) over the screening period. FDG-PET obtained in 6.5% of subjects had 84% sensitivity and 90% specificity for malignant lesions. FNAB obtained in 2.4% of subjects showed 90% sensitivity and 88% specificity. Positivity of both FDG-PET and FNAB invariably predicted malignancy. Surgery for benign lesions was performed on four subjects (10% of procedures) but followed protocol violations on three subjects. Conclusions: High-risk subjects recruited by mail who entered LDCT screening showed a high and stable compliance. Efficacy of screening is, however, weakened by low detection rate and specificity. Adhesion to management protocol might lessen surgery for benign lesions.Journal of Thoracic Oncology 07/2013; 8(7):866-875. DOI:10.1097/JTO.0b013e31828f68d6 · 5.80 Impact Factor
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ABSTRACT: We present a fast and robust atlas-based algorithm for labeling airway trees, using geodesic distances in a geometric tree-space. Possible branch label configurations for an unlabeled airway tree are evaluated using distances to a training set of labeled airway trees. In tree-space, airway tree topology and geometry change continuously, giving a natural automatic handling of anatomical differences and noise. A hierarchical approach makes the algorithm efficient, assigning labels from the trachea and downwards. Only the airway centerline tree is used, which is relatively unaffected by pathology. The algorithm is evaluated on 80 segmented airway trees from 40 subjects at two time points, labeled by 3 medical experts each, testing accuracy, reproducibility and robustness in patients with Chronic Obstructive Pulmonary Disease (COPD). The accuracy of the algorithm is statistically similar to that of the experts and not significantly correlated with COPD severity. The reproducibility of the algorithm is significantly better than that of the experts, and negatively correlated with COPD severity. Evaluation of the algorithm on a longitudinal set of 8724 trees from a lung cancer screening trial shows that the algorithm can be used in large scale studies with high reproducibility, and that the negative correlation of reproducibility with COPD severity can be explained by missing branches, for instance due to segmentation problems in COPD patients. We conclude that the algorithm is robust to COPD severity given equally complete airway trees, and comparable in performance to that of experts in pulmonary medicine, emphasizing the suitability of the labeling algorithm for clinical use.IEEE Transactions on Medical Imaging 12/2014; DOI:10.1109/TMI.2014.2380991 · 3.80 Impact Factor
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ABSTRACT: We present a novel descriptor for the characterization of pulmonary nodules in computed tomography (CT) images. The descriptor encodes information on nodule morphology and has scale-invariant and rotation-invariant properties. Information on nodule morphology is captured by sampling intensity profiles along circular patterns on spherical surfaces centered on the nodule, in a multi-scale fashion. Each intensity profile is interpreted as a periodic signal, where the Fourier transform is applied, obtaining a spectrum. A library of spectra is created and labeled via unsupervised clustering, obtaining a Bag-of- Frequencies, which is used to assign each spectra a label. The descriptor is obtained as the histogram of labels along all the spheres. Additional contributions are a technique to estimate the nodule size, based on the sampling strategy, as well as a technique to choose the most informative plane to cut a 2-D view of the nodule in the 3-D image. We evaluate the descriptor on several nodule morphology classification problems, namely discrimination of nodules versus vascular structures and characterization of spiculation. We validate the descriptor on data from European screening trials NELSON and DLCST and we compare it with state-of-the-art approaches for 3-D shape description in medical imaging and computer vision, namely SPHARM and 3-D SIFT, outperforming them in all the considered experiments.IEEE Transactions on Medical Imaging 11/2014; DOI:10.1109/TMI.2014.2371821 · 3.80 Impact Factor