Faculty Bibliography

PURPOSE: To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates. MATERIALS AND METHODS: This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years+/-1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient+/-1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years+/-0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth. RESULTS: Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days+/-158, compared with radiologic or clinical diagnosis at 344 days+/-284. CONCLUSION: A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.

BACKGROUND AND PURPOSE: To determine the optimal method of targeting breast and regional nodes in selected breast cancer patients after axillary dissection, we compared the results of IMRT versus no IMRT, and CT-informed versus clinically-placed fields, in supine and prone positions. MATERIALS AND METHODS: Twelve consecutive breast cancer patients simulated both prone and supine provided the images for this study. Four techniques were used to target breast, level III axilla, and supraclavicular fossa in either position: a traditional three-field three-dimensional conformal radiotherapy (3DCRT) plan, a four-field 3DCRT plan using a posterior axillary boost field, and two techniques using a CT-informed target volume consisting of an optimized 3DCRT plan (CT-planned 3D) and an intensity-modulated radiotherapy (IMRT) plan. The prescribed dose was 50Gy in 25 fractions. RESULTS: CT-planned 3D and IMRT techniques improved nodal PTV coverage. Supine, mean nodal PTV V50 was 50% (3-field), 59% (4-field), 92% (CT-planned 3D), and 94% (IMRT). Prone, V50 was 29% (3-field), 42% (4-field), 97% (CT-planned 3D), and 95% (IMRT). Prone positioning, compared to supine, and IMRT technique, compared to 3D, lowered ipsilateral lung V20. CONCLUSIONS: Traditional 3DCRT plans provide inadequate nodal coverage. Prone IMRT technique resulted in optimal target coverage and reduced ipsilateral lung V20

Advances in multidetector technology have made dual-energy computed tomography (CT) imaging possible. Dual-energy CT imaging enables tissue characterization in addition to morphologic evaluation of imaged regions. This article reviews current and potential CT technology, technical and workflow considerations when performing dual-energy CT, and clinical applications in the thorax, with an emphasis on the knowledge gained so far

OBJECTIVE: : To describe pathologic findings in symptomatic World Trade Center-exposed local workers, residents, and cleanup workers enrolled in a treatment program. METHODS: : Twelve patients underwent surgical lung biopsy for suspected interstitial lung disease (group 1, n = 6) or abnormal pulmonary function tests (group 2, n = 6). High-resolution computed axial tomography and pathologic findings were coded. Scanning electron microscopy with energy-dispersive x-ray spectroscopy was performed. RESULTS: : High-resolution computed axial tomography showed reticular findings (group 1) or normal or airway-related findings (group 2). Pulmonary function tests were predominantly restrictive. Interstitial fibrosis, emphysematous change, and small airway abnormalities were seen. All cases had opaque and birefringent particles within macrophages, and examined particles contained silica, aluminum silicates, titanium dioxide, talc, and metals. CONCLUSIONS: : In symptomatic World Trade Center-exposed individuals, pathologic findings suggest a common exposure resulting in alveolar loss and a diverse response to injury

Pulmonary nodule detection and characterization continue to improve with technological advancements. The noninvasive methods available for assisting in nodule detection and for characterizing nodules as benign, malignant, or indeterminate will be discussed. Evidence-based guidelines will be reviewed to help guide the appropriate management of pulmonary nodules

Endovascular techniques have emerged as a minimally invasive alternative for the repair of the descending thoracic aorta, especially in high-risk patients. Multidetector computed tomography has a pivotal role, specifically in determining the candidacy or exclusion of patients for thoracic endovascular aortic repair and preoperative planning. In addition, multidetector computed tomography is used for follow-up assessment of the postsurgical aorta, so that potentially fatal complications can be correctly diagnosed and treated in a timely manner. In this pictorial review, we focus on the preoperative assessment of the pathologic aorta and evaluation after thoracic endovascular aortic repair

Purpose: Many potential lung cancers start out as small pulmonary nodules showing up as incidental findings on chest radiograph or computed tomography (CT) scans. Diagnosis is confirmed via biopsy, usually involving broncoscopy or CT-guided biopsy. However, these are invasive procedures that expose patients to additional risks. An alternative mode of tumor detection lies in administering successive chest CT scans. This has the advantage of avoiding those risks associated with biopsy. Overall, there is growing evidence for the effectiveness of low-dose CT for lung cancer screening. Morphology is an important indicator of malignant potential for lung nodules detected at CT. Automated methods for morphology assessment have previously been described for breast cancer visualized on mammography [1]. The most common measure of nodule shape is area-to-perimeter-length ratio (APR), low APR values being associated with spiculated or lobulated shape. APR is a static measure and thus highly susceptible to alterations by random noise and artifacts in image acquisition. We introduce and analyze the self-overlap (SO) method as a dynamic automated morphological detection scheme. SO measures the rate of change of nodule masks as a function of the radius of the blurring kernel. In other words, SO measures the degree to which a nodule's shape changes or stays intact upon successive pixel averaging that blurs the original image. Irregularities at the surface mean that a significant number of high-attenuation pixels (representing solid nodular tissue) are surrounded by low-attenuation pixels (representing air). Averaging each pixel with its neighboring pixels thus serves to trim back lobulations and spiculations from a nodule image. Hence, comparedto smooth nodules, lobulated and spiculated nodules are subject to more of this trimming upon successive averaging, so that their shape changes more, resulting in lower SO values. Due to its dynamical nature, we hypothesized that SO is more resilient to random image noise than APR. Methods: In experiment 1 we compare our algorithm with APR for nodules simulated using a spherical harmonic model (degree = 0-7) rasterized and contaminated with random noise. In experiment 2 we compare the new measure with a consensus of two expert morphology ratings of 119 nodules from clinical CT exams. Results: Experiment 1 shows that both methods display the desired trend in that APR and SO both decrease with increasing spherical harmonic degree-meaning more lobulations. As such both methods serve as measures of surface smoothness. However, SO displays significantly greater robustness to CT image noise; for both methods, we calculate variability as standard deviation over mean. We find that APR's variability in the face of random noise is on the order of ten times that of SO. This finding suggests that SO is much more robust than APR to the effects of random noise. Using a logistic regression model, in experiment 2 we achieved 89.9% agreement with the consensus assessment of two expert radiologists, versus 87.4% for APR. Conclusion: Simulation nodules show that both our dynamic method (SO) and a representative static method (APR) for automated lung nodule surface morphology determination yield clear trends as functions of surface smoothness. Hence both methods can, with proper fitting and cutoff selection, yield faithful predictions that have over 80% agreement with expert assessment. However, when the simulation nodules are subjected to random noise, SO yields much more consistent and reproducible results than APR. Overall, we conclude that our method, due to its robustness to the random noise and CT artifacts that can plague nodule images, is well suited for clinical application