• Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA.
• Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA.
• University of Sao Paolo, Sao Paolo, Brazil.
• University of Washington, Seattle WA, USA.
• Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA.
• Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
• Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
• University of Vermont College of Medicine, Burlington, VT, USA.
• Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
• University of Vermont, Burlington, VT, USA.

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.