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

C Matthew Kinsey¹, Ehab Billatos², Vitor Mori³, Ben Tonelli⁴, Bernard F Cole⁵, Fenghai Duan⁶, Helga Marques⁷, Isaac de la Bruere⁸, Jorge Onieva⁹, Rubén San José Estépar⁹, Alyx Cleveland¹⁰, Dan Idelkope¹¹, Chris Stevenson¹², Jason H T Bates¹, Denise Aberle¹³, Avi Spira¹⁴, George Washko¹⁵, Raúl San José Estépar⁹

Affiliations

¹ Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA.
² Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA.
³ University of Sao Paolo, Sao Paolo, Brazil.
⁴ University of Washington, Seattle WA, USA.
⁵ Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA.
⁶ Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
⁷ Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
⁸ University of Vermont College of Medicine, Burlington, VT, USA.
⁹ Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁰ University of Vermont, Burlington, VT, USA.
¹¹ Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.
¹² Janssen Pharmaceuticals, Titusville, NJ, USA.
¹³ David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
¹⁴ The Pulmonary Unit, Boston Medical Center, Boston, MA, USA.
¹⁵ Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA.

PMID: 34422349
PMCID: PMC8339782
DOI: 10.21037/jtd-20-3093

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

C Matthew Kinsey et al. J Thorac Dis. 2021 Jul.

. 2021 Jul;13(7):4207-4216.

doi: 10.21037/jtd-20-3093.

Authors

Affiliations

¹ Division of Pulmonary and Critical Care, University of Vermont Medical Center, Burlington, VT, USA.
² Section of Pulmonary and Critical Care Medicine, Department of Medicine, Boston University, Boston, MA, Boston Medical Center, Boston, MA, USA.
³ University of Sao Paolo, Sao Paolo, Brazil.
⁴ University of Washington, Seattle WA, USA.
⁵ Department of Mathematics and Statistics, University of Vermont, Burlington, VT, USA.
⁶ Department of Biostatistics and Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
⁷ Center for Statistical Sciences, Brown University School of Public Health, Providence, RI, USA.
⁸ University of Vermont College of Medicine, Burlington, VT, USA.
⁹ Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
¹⁰ University of Vermont, Burlington, VT, USA.
¹¹ Geisel School of Medicine at Dartmouth College, Hanover, NH, USA.
¹² Janssen Pharmaceuticals, Titusville, NJ, USA.
¹³ David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
¹⁴ The Pulmonary Unit, Boston Medical Center, Boston, MA, USA.
¹⁵ Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, USA.

PMID: 34422349
PMCID: PMC8339782
DOI: 10.21037/jtd-20-3093

Abstract

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.

Keywords: Lung nodule; nodule location; non-small cell lung cancer.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/jtd-20-3093). CMK reports grant funding from the NIH, consulting fees and clinical trial funding from Johnson and Johnson. CMK is a consultant and equity holder for Quantitative Imaging Solutions, and sits on the Board of Directors of the American Association of Bronchology and Interventional Pulmonology and the Prevention Committee of the Alliance for Oncology Clinical Trials. EB reports grant funding from the Department of Defense, Johnson and Johnson, and the National Cancer Institute. VM reports funding from the Damon Runyon Cancer Research Foundation and CAPES (Brazil). HM reports grant funding from Novartis, Janssen, and the Department of Defense. BT, AC, and ID, and Ruben SJ report consulting fees from Quantitative Imaging Services. CS reports Johnson and Johnson funding for DECAMP, and is employed by and owns stock in Johnson and Johnson. DA reports grant funding from American College of Radiology, Boston University, the NIH, and the Kaiser Foundation/PCORI, honoraria from the NIH, Department of Defense, International Symposium on Clinical Update in Respiratory Medicine, Cancer Research UC, American Lung Association, Japanese Society for CT screening, and grant support from the Early Detection Research Network, Molecular and Cellular Characterization of Screen-Detected Lesions Consortium, Department of Defense, International Lung Cancer Conference, Early Detection of Cancer Conference, and the Molecular and Cellular Characterization of Screen-Detected Lesions Consortium, support for meetings/travel from the American Institute for Medical and Biological Engineering, Cleveland Clinic, Specialized Programs of Research Excellence, and the International Association for the Study of Lung Cancer. JHB reports consulting fees from Johnson and Johnson. AS is an employee of Johnson and Johnson. GRW is an equity holder in Quantitative Imaging Solutions. RSJ reports grants from the NIH, a patent pending in lung cancer risk assessment, and is an equity holder of Quantitative Imaging Solutions. The authors have no other conflicts of interest to declare.

Figures

**Figure 1**
Geospatial location of malignant (red) and benign (blue) nodules. Labels on the axes represent distances (in mm) from the carina along each direction, normalized to lung volume. The X location is determined by the lateral position, the Y by the anterior-posterior position, and the Z by the superior-inferior position of the nodule.

**Figure 2**
The risk of malignancy in an indeterminate nodule based on superior-inferior location in the lung. The heat map demonstrates that some slices (5 mm thickness) carry risks for lung cancer as high as 30%.

See this image and copyright information in PMC

References

1. National Lung Screening Trial Research Team , Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409. 10.1056/NEJMoa1102873 - DOI - PMC - PubMed
1. Kazerooni EA, Armstrong MR, Amorosa JK, et al. ACR CT accreditation program and the lung cancer screening program designation. J Am Coll Radiol 2015;12:38 42. 10.1016/j.jacr.2014.10.002 - DOI - PubMed
1. Pinsky PF, Gierada DS, Black W, et al. Performance of Lung-RADS in the National Lung Screening Trial: a retrospective assessment. Ann Intern Med 2015;162:485 491. 10.7326/M14-2086 - DOI - PMC - PubMed
1. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest 2013;143:e93S-e120S. - PMC - PubMed
1. Swensen SJ, Silverstein MD, Ilstrup DM, et al. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med 1997;157:849 855. 10.1001/archinte.1997.00440290031002 - DOI - PubMed

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

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

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