A simple assessment of lung nodule location for reduction in unnecessary invasive procedures.

BACKGROUND: CT screening for lung cancer results in a significant mortality reduction but is complicated by invasive procedures performed for evaluation of the many detected benign nodules. The purpose of this study was to evaluate measures of nodule location within the lung as predictors of malignancy. METHODS: We analyzed images and data from 3,483 participants in the National Lung Screening Trial (NLST). All nodules (4-20 mm) were characterized by 3D geospatial location using a Cartesian coordinate system and evaluated in logistic regression analysis. Model development and probability cutpoint selection was performed in the NLST testing set. The Geospatial test was then validated in the NLST testing set, and subsequently replicated in a new cohort of 147 participants from The Detection of Early Lung Cancer Among Military Personnel (DECAMP) Consortium. RESULTS: The Geospatial Test, consisting of the superior-inferior distance (Z distance), nodule diameter, and radial distance (carina to nodule) performed well in both the NLST validation set (AUC 0.85) and the DECAMP replication cohort (AUC 0.75). A negative Geospatial Test resulted in a less than 2% risk of cancer across all nodule diameters. The Geospatial Test correctly reclassified 19.7% of indeterminate nodules with a diameter over 6mm as benign, while only incorrectly classifying 1% of cancerous nodules as benign. In contrast, the parsimonious Brock Model applied to the same group of nodules correctly reclassified 64.5% of indeterminate nodules as benign but resulted in misclassification of a cancer as benign in 18.2% of the cases. Applying the Geospatial test would result in reducing invasive procedures performed for benign lesions by 11.3% with a low rate of misclassification (1.3%). In contrast, the Brock model applied to the same group of patients results in decreasing invasive procedures for benign lesion by 39.0% but misclassifying 21.1% of cancers as benign. CONCLUSIONS: Utilizing information about geospatial location within the lung improves risk assessment for indeterminate lung nodules and may reduce unnecessary procedures. TRIAL REGISTRATION: NCT00047385, NCT01785342.

Pulmonary vascular density: comparison of findings on computed tomography imaging with histology.

BACKGROUND: Exposure to cigarette smoke has been shown to lead to vascular remodelling. Computed tomography (CT) imaging measures of vascular pruning have been associated with pulmonary vascular disease, an important morbidity associated with smoking. In this study we compare CT-based measures of distal vessel loss to histological vascular and parenchymal changes. METHODS: A retrospective review of 80 patients who had undergone lung resection identified patients with imaging appropriate for three-dimensional (3D) vascular reconstruction (n=18) and a second group for two-dimensional (2D) analysis (n=19). Measurements of the volume of the small vessels (3D) and the cross-sectional area of the small vessels (<5 mm2 cross-section) were computed. Histological measures of cross-sectional area of the vasculature and loss of alveoli septa were obtained for all subjects. RESULTS: The 2D cross-sectional area of the vasculature on CT imaging was associated with the histological vascular cross-sectional area (r=0.69; p=0.001). The arterial small vessel volume assessed by CT correlated with the histological vascular cross-sectional area (r=0.50; p=0.04), a relationship that persisted even when adjusted for CT-derived measures of emphysema in a regression model. CONCLUSIONS: Loss of small vessel volume in CT imaging of smokers is associated with histological loss of vascular cross-sectional area. Imaging-based quantification of pulmonary vasculature provides a noninvasive method to study the multiscale effects of smoking on the pulmonary circulation.

3D Pulmonary Artery Segmentation from CTA Scans Using Deep Learning with Realistic Data Augmentation.

The characterization of the vasculature in the mediastinum, more specifically the pulmonary artery, is of vital importance for the evaluation of several pulmonary vascular diseases. Thus, the goal of this study is to automatically segment the pulmonary artery (PA) from computed tomography angiography images, which opens up the opportunity for more complex analysis of the evolution of the PA geometry in health and disease and can be used in complex fluid mechanics models or individualized medicine. For that purpose, a new 3D convolutional neural network architecture is proposed, which is trained on images coming from different patient cohorts. The network makes use a strong data augmentation paradigm based on realistic deformations generated by applying principal component analysis to the deformation fields obtained from the affine registration of several datasets. The network is validated on 91 datasets by comparing the automatic segmentations with semi-automatically delineated ground truths in terms of mean Dice and Jaccard coefficients and mean distance between surfaces, which yields values of 0.89, 0.80 and 1.25 mm, respectively. Finally, a comparison against a Unet architecture is also included.

Ventricular Geometry From Non-contrast Non-ECG-gated CT Scans: An Imaging Marker of Cardiopulmonary Disease in Smokers.

RATIONALE AND OBJECTIVES: Imaging-based assessment of cardiovascular structure and function provides clinically relevant information in smokers. Non-cardiac-gated thoracic computed tomographic (CT) scanning is increasingly leveraged for clinical care and lung cancer screening. We sought to determine if more comprehensive measures of ventricular geometry could be obtained from CT using an atlas-based surface model of the heart. MATERIALS AND METHODS: Subcohorts of 24 subjects with cardiac magnetic resonance imaging (MRI) and 262 subjects with echocardiography were identified from COPDGene, a longitudinal observational study of smokers. A surface model of the heart was manually initialized, and then automatically optimized to fit the epicardium for each CT. Estimates of right and left ventricular (RV and LV) volume and free-wall curvature were then calculated and compared to structural and functional metrics obtained from MRI and echocardiograms. RESULTS: CT measures of RV dimension and curvature correlated with similar measures obtained using MRI. RV and LV volume obtained from CT inversely correlated with echocardiogram-based estimates of RV systolic pressure using tricuspid regurgitation jet velocity and LV ejection fraction respectively. Patients with evidence of RV or LV dysfunction on echocardiogram had larger RV and LV dimensions on CT. Logistic regression models based on demographics and ventricular measures from CT had an area under the curve of >0.7 for the prediction of elevated right ventricular systolic pressure and ventricular failure. CONCLUSIONS: These data suggest that non-cardiac-gated, non-contrast-enhanced thoracic CT scanning may provide insight into cardiac structure and function in smokers.

Arterial and Venous Pulmonary Vascular Morphology and Their Relationship to Findings in Cardiac Magnetic Resonance Imaging in Smokers.

OBJECTIVE: Prior work has described the relationship between pulmonary vascular pruning on computed tomography (CT) scans and metrics of right-sided heart dysfunction in smokers. In this analysis, we sought to look at pruning on a lobar level, as well as examine the effect of the arterial and venous circulation on this association. METHODS: Automated vessel segmentation applied to noncontrast CT scans from the COPDGene Study in 24 subjects with cardiac magnetic resonance imaging scans was used to create a blood volume distribution profile. These vessels were then manually tracked to their origin and characterized as artery or vein. RESULTS: Assessment of pruning on a lobar level revealed associations between pruning and right ventricular function previously not observed on a global level. The right ventricular mass index, the right ventricular end-systolic volume index, and pulmonary arterial-to-aorta ratio were associated with both arterial and venous pruning, whereas right ventricular ejection fraction was associated with only arterial pruning. CONCLUSIONS: Lobar assessment and segmentation of the parenchymal vasculature into arterial and venous components provide additional information about the relationship between loss of vasculature on CT scans and right ventricular dysfunction.