Faculty Bibliography

Individuals concerned about their risk of lung cancer are recommended to talk with their physicians about computed tomographic screening for lung cancer. To provide the necessary information, the survival benefit of the screening, specific to a particular person for a particular round of screening, is needed. The probability of survival gain from the first, baseline, round of screening was addressed as the product of: 1) the screening resulting in a diagnosis of lung cancer; 2) not dying from some other cause for a sufficiently long period of time; and 3) cure resulting from pre-symptomatic treatment of lung cancer. These probabilities were estimated using the International Early Lung Cancer Action Program data on individuals aged 40-85 yrs with a cigarette smoking history of 0-150 pack-yrs. The estimated probability of survival gain ranged from 0.4% for a 60-yr-old with a 10-pack-yr smoking history who quit smoking 20 yrs ago, to 3.1% for a 70-yr-old current smoker with a 100 pack-yr history and 2.0% for an 85-yr-old current smoker with a 150-pack-yr history. When seeking counsel about initiation of screening for lung cancer, an estimate of the probability of survival gain from the first round of computed tomographic screening, specific to the person's age and history of smoking, can be provided

Computed tomography (CT) images of the lungs provide high resolution views of the airways. Quantitative measurements such as lumen diameter and wall thickness help diagnose and localize airway diseases, assist in surgical planning, and determine progress of treatment. Automated quantitative analysis of such images is needed due to the number of airways per patient. We present an approach involving dynamic programming coupled with boundary-specific cost functions that is capable of differentiating inner and outer borders. The method allows for precise delineation of the inner lumen and outer wall. The results are demonstrated on synthetic data, evaluated on human datasets compared to human operators, and verified on phantom CT scans to sub-voxel accuracy

PURPOSE: To identify, by using a chest phantom, whether vessels that contact lung nodules measuring less than 5 mm in diameter will affect nodule volume assessment. MATERIALS AND METHODS: Forty synthetic nodules (20 with ground-glass attenuation and 20 with solid attenuation) that measured less than 5 mm in diameter were placed into a chest phantom either adjacent to (n = 30) or isolated from (n = 10) synthetic vessels. Nodules were imaged by using low-dose (20 mAs) and diagnostic (120 mAs) multi-detector row computed tomography (CT). Nodules that were known to lie in direct contact with vessels were confirmed by visual inspection. Nontargeted 1.25 x 1.00-mm sections were analyzed with a three-dimensional computer-assisted method for measuring nodule volume. A mixed-model analysis of variance was used to examine the influence of several factors (eg, the presence of adjacent vessels; tube current-time product; and nodule attenuation, diameter, and location) on measurement error. RESULTS: The mean absolute error (MAE) for all nodules adjacent to vessels was 2.3 mm(3), which was higher than the MAE for isolated nodules (1.9 mm(3)) (P < .001). This difference proved significant only for diagnostic CT (2.2 mm(3) for nodules adjacent to vessels vs 1.3 mm(3) for nodules isolated from vessels) (P < .05). A larger MAE was noted for nodules with ground-glass attenuation (2.3 mm(3)) versus those with solid attenuation (2.0 mm(3)), for increasing nodule volume (1.66 mm(3) for nodules smaller than 20 mm(3) vs 2.83 mm(3) for nodules larger than 40 mm(3)), and for posterior nodule location (P < .05). CONCLUSION: The presence of a vessel led to a small yet significant increase in volume error on diagnostic-quality images. This represents less than one-third of the overall error, even for nodules larger than 40 mm(3) or approximately 4 mm in diameter. This increase, however, may be more important for smaller nodules with errors of less than 3 mm(3)

The evaluation of patients presenting with multinodular pulmonary disease provides an important clinical challenge for physicians. The differential diagnosis includes an extensive list of benign and malignant processes making the management of these cases frequently problematic. With the introduction of high-resolution CT (HRCT) scanning, the ability to assess various patterns of diffuse multinodular disease has evolved into an essential part of the diagnostic process. The purpose of this article is to develop an approach to the diagnosis of multinodular parenchymal disease using HRCT scan pattern recognition as a point of departure

Pulmonary embolism (PE) is a life-threatening disease, requiring rapid diagnosis and treatment. Contrast enhanced computed tomographic (CT) images of the lungs allow physicians to confirm or rule out PE, but the large number of images per study and the complexity of lung anatomy may cause some emboli to be overlooked. We evaluated a novel three-dimensional (3D) visualization technique for detecting PE, and compared it with traditional 2D axial interpretation. Three readers independently marked 10 cases using the 3D method, and a separate interpretation was performed at a later date using only source axial images. An experienced thoracic radiologist adjudicated all marks, classifying clots according to location and confidence. There were a total of 8 positive examinations with 69 validated emboli. 44 (64%) of the clots were segmental while 12 (17%) proved subsegmental. Using the traditional 2D method for examination, readers detected a mean of 45 PE for 66% sensitivity. Using the 3D method, readers detected a mean of 35 PE (50% sensitivity). Combining both methods, readers detected a mean of 51 PE (74% sensitivity), significantly higher than either single method (p < 0.001). Considered by arterial level, significant improvement was observed for detection of segmental and subsegmental clots (p < 0.001) when comparing combined reading with either single method. The mean number of false positives per patient was 0.23 for both 2D and 3D readings and 0.4 for combined reading. 3D visualization of pulmonary arteries allowed readers to detect a significant number of additional emboli not detected during 2D axial interpretations and thus may lead to a more accurate diagnosis of PE

Lung nodules are detected very commonly on computed tomographic (CT) scans of the chest, and the ability to detect very small nodules improves with each new generation of CT scanner. In reported studies, up to 51% of smokers aged 50 years or older have pulmonary nodules on CT scans. However, the existing guidelines for follow-up and management of noncalcified nodules detected on nonscreening CT scans were developed before widespread use of multi-detector row CT and still indicate that every indeterminate nodule should be followed with serial CT for a minimum of 2 years. This policy, which requires large numbers of studies to be performed at considerable expense and with substantial radiation exposure for the affected population, has not proved to be beneficial or cost-effective. During the past 5 years, new information regarding prevalence, biologic characteristics, and growth rates of small lung cancers has become available; thus, the authors believe that the time-honored requirement to follow every small indeterminate nodule with serial CT should be revised. In this statement, which has been approved by the Fleischner Society, the pertinent data are reviewed, the authors' conclusions are summarized, and new guidelines are proposed for follow-up and management of small pulmonary nodules detected on CT scans

PURPOSE: To evaluate the effect of two-dimensional wavelet-based computed tomographic (CT) image compression according to the Joint Photographic Experts Group (JPEG) 2000 standard on computer-assisted assessment of nodule volume. MATERIALS AND METHODS: This HIPAA-compliant study was approved by the research board at the authors' institution; patients' informed consent was not required. Fifty-one nodules in 23 patients (seven men, 16 women; mean age, 59 years; age range, 39-75 years) were selected on low-dose CT scans that were compressed to levels of 10:1, 20:1, 30:1, and 40:1 by using a two-dimensional JPEG 2000 wavelet-based image compression method. Nodules were classified according to size (< or = 5 mm or > 5 mm in diameter), location (central, peripheral, or abutting pleura or fissures), and attenuation (solid, calcified, or subsolid). Regions of interest were placed on the original images and transposed onto compressed images. Nodule volumes on original (noncompressed) and compressed images were measured by using a computer-assisted method. A mixed-model analysis of variance was conducted for statistical evaluation. RESULTS: Nodule volumes averaged 388.1 mm3 (range, 34-3474 mm3). There were three calcified, 33 solid noncalcified, and 15 subsolid nodules (13 with ground-glass attenuation). Average volume decreased with increasing compression level, to 383 mm3 (10:1), 370 mm3 (20:1), 360 mm3 (30:1), and 354 mm3 (40:1). No significant difference was identified between measurements obtained on original images and those compressed to a level of 10:1. Significant differences were noted, however, between original images and those compressed to a level of 20:1 or greater (P < .05). Compression level significantly interacted with nodule size, location, and attenuation (P < .001). The effect of compression was greater for nodules with ground-glass attenuation than for those with higher attenuation values. The difference in mean volumes between original images and those compressed to a level of 20:1 was 34.9 mm3 for nodules with ground-glass attenuation, compared with 8.3 mm3 for higher-attenuation nodules, a 4.2-fold difference. CONCLUSION: Nodule volumes measured on images compressed to a level of 20:1 differed significantly from those measured on noncompressed images, especially for nodules with ground-glass attenuation. This difference could affect the assessment of nodule change in size as measured with computer-assisted methods

Ground glass nodules (GGNs) have proved especially problematic in lung cancer diagnosis, as despite frequently being malignant they have extremely slow growth rates. In this work, the GGN segmentation results of a computer-based method were compared with manual segmentation performed by two dedicated chest radiologists. CT volumes of 3 patients were acquired by multi-slice CT. 21 pure or mixed GGNs were identified and independently segmented by the computer-based method and by two readers. The computer-based method is initialized by a click point, and uses a Markov random field (MRF) model for segmentation. While the intensity distribution varies for different GGNs, the. intensity model used in MRF is adapted for each nodule based on initial estimates. This method was run three times for each nodule using different click points to evaluate consistency. In this work, consistency was defined by the overlap ratio (overlap volume/mean volume). The consistency of the computer-based method with different initial points, with a mean overlap ratio of 0.96 +/- 0.02 (95% confidence interval on mean), was significantly higher than the inter-observer consistency between the two radiologists, indicated by a mean overlap ratio of 0.73 +/- 0.04. The computer consistency was also significantly higher than the intra-observer consistency of two measurements from the same radiologist, indicated by an overlap ratio of 0.69 +/- 0.05 (p-value &lt; 1E-05). The concordance of the computer with the expert interpretation demonstrated a mean overlap ratio of 0.69 +/- 0.05. As shown by our data, the consistency provided by the computer-based method is significantly higher than between observers, and the accuracy of the method is no worse than that of one physician's accuracy with respect to another, allowing more reproducible assessment of nodule growth.