Lung cancer is the world's leading cause of cancer death. Many recent investigations focus attention on the molecular pathogenesis of the disease, risk assessment, and chemoprevention (1). Advances in clinical and genetic risk assessment for lung cancer are central to the progress of research in the areas of pathogenesis and chemoprevention. Although chronic obstructive pulmonary disease (COPD) has long been well established to be associated with lung cancer risk (2), it is not yet known which aspects of this risk can be attributed to cooperative or shared genetic susceptibility and/or activation of inflammatory pathways. In this issue of the Journal (pp. 738744), Wilson and colleagues provide additional, important information regarding the association of radiographic emphysema and airflow obstruction with lung cancer (3).

Segmentation of lungs using high-resolution computer tomographic images in the setting of diffuse lung diseases is a major challenge in medical image analysis. Threshold-based techniques tend to leave out lung regions that have increased attenuation, such as in the presence of interstitial lung disease. In contrast, streak artifacts can cause the lung segmentation to leak into the chest wall. The purpose of this work was to perform segmentation of the lungs using a technique that selects an optimal threshold for a given patient by comparing the curvature of the lung boundary to that of the ribs.

Lung disease has become the leading cause of mortality and morbidity in scleroderma (SSc) patients. The frequency, nature, and progression of interstitial lung disease seen on high-resolution CT (HRCT) scans in patients with diffuse SSc (dcSSc) compared with those with limited SSc (lcSSc) has not been well characterized.

Computerized classification techniques have been developed to offer accurate and robust pattern recognition in interstitial lung disease using texture features. However, these techniques still present challenges when analyzing computed tomographic (CT) image data from multiprotocols because of disparate acquisition protocols or from standardized, multicenter clinical trials because of noise variability. Our objective is to investigate the utility of denoising thin section CT image data to improve the classification of scleroderma disease patterns. The patterns are lung fibrosis (LF), groundglass (GG), honeycomb (HC), or normal lung (NL) within small regions of interest (ROIs).

Image-guided thoracic interventional procedures have continued to evolve. Over the past decade, the accepted have continued to be refined and re-invented, and the emerging have experienced tremendously quick diffusion of innovation, crossing specialty divides. Certainly, advancements in imaging and technology have been highly visible and easily citable factors but, more poignantly, the spirit of innovation in the individual and collective and their acceptance of and application of new and emerging technologies have been the true driving force behind this movement. The benefit is clearly and increasingly evident in daily practice, specifically in pulmonary and critical care medicine, the focus of this issue.

Percutaneous radiofrequency ablation yields high proportions of sustained complete responses in properly selected patients with pulmonary malignancies, and is associated with acceptable morbidity. Randomised controlled trials comparing radiofrequency ablation with standard non-surgical treatment options are warranted.

Computed tomography (CT) imaging is playing an increasingly important role in cancer detection, diagnosis, and lesion characterization, and it is the most sensitive test for lung nodule detection. Interpretation of lung nodules involves characterization and integration of clinical and other imaging information. Advances in lung nodule management using CT require optimization of CT data acquisition, postprocessing tools, and computer-aided diagnosis (CAD). The goal of CAD systems being developed is to both assist radiologists in the more sensitive detection of nodules and noninvasively differentiate benign from malignant lesions; the latter is important given that malignant lesions account for between 1% and 11% of pulmonary nodules. The aim of this review is to summarize the current state of the art regarding CAD techniques for the detection and characterization of solitary pulmonary nodules and their potential applications in the clinical workup of these lesions.

Lung cancer is the leading cause of cancer death in the United States. Nonsmall cell lung cancer accounts for 75% to 80% of all lung cancers. There is an impetus to find a screening test that can detect nonsmall cell lung cancer in its early preclinical stages, when surgical resection is most likely to reduce lung cancer mortality. Although earlier randomized controlled trials of lung cancer screening using chest radiography and sputum cytology failed to show reduced lung cancer mortality, CT is a much more sensitive test for detecting small lung nodules, and has generated considerable enthusiasm as a potential contemporary screening tool for lung cancer.

RATIONALE: The presence of inflammatory cells on bronchoalveolar lavage is often used to predict disease activity and the need for therapy in systemic sclerosis-associated interstitial lung disease.
OBJECTIVES: To evaluate whether lavage cellularity identifies distinct subsets of disease and/or predicts cyclophosphamide responsiveness.