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Hiren J. Mehta, MD; Paul J. Nietert, PhD; Nichole T. Tanner, MD, FCCP; James G. Ravenel, MD; Gerard A. Silvestri, MD, FCCP
Author and Funding Information

From the Division of Pulmonary, Critical Care, and Sleep Medicine (Dr Mehta), University of Florida College of Medicine; and Department of Public Health Sciences (Dr Nietert), Division of Pulmonary, Critical Care, Allergy and Sleep Medicine (Drs Tanner and Silvestri), and Department of Radiology and Radiological Sciences (Dr Ravenel), Medical University of South Carolina.

CORRESPONDENCE TO: Hiren J. Mehta, MD, Division of Pulmonary, Critical Care, and Sleep Medicine, University of Florida College of Medicine, 1600 SW Archer Rd, M 452, Gainesville, FL 32610; e-mail: hiren.mehta@medicine.ufl.edu


FINANCIAL/NONFINANCIAL DISCLOSURES: The authors have reported to CHEST that no potential conflicts of interest exist with any companies/organizations whose products or services may be discussed in this article.

Reproduction of this article is prohibited without written permission from the American College of Chest Physicians. See online for more details.


Chest. 2014;146(2):e70. doi:10.1378/chest.14-0915
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Published online
To the Editor:

We thank Dr Hochhegger and colleagues for their interest in our study on the utility of nodule volume in the context of malignancy prediction for small pulmonary nodules.1 The letter raises several important points, including that there may be significant interobserver and intraobsever variability in nodule volume measurement, which itself depends on various factors like image acquisition, reconstruction, nodule characteristics, and the use of a segmentation algorithm.2,3 We agree that variability in volume measurement is a significant challenge for introducing semiautomated volumetric software into daily practice.

Nodule volumes, however, can be consistently reproduced using standardized protocols. Gietema and colleagues4 demonstrated that under certain conditions (using standard CT image slice thickness, fixed energy, scan time of 10 s, and a soft kernel for reconstruction), a high degree (r = 0.99) of interobserver correlation can be achieved for small- and intermediate-sized nodules using semiautomated volumetric software. It has also been shown that variability in volume measurements is related to nodule size, morphology, and location. For small- to intermediate-sized solid nodules surrounded by lung parenchyma, volumetric measurements are reproducible. Wang and colleagues5 demonstrated that as long as consistent reconstruction parameters are applied using soft kernel, the volume measurements are very reproducible. In their study, no statistically significant benefit for consensus double reading on semiautomated volumetry was found. Based on these findings, the fourth screening round image reading in the Dutch-Belgian randomized lung cancer screening trial (NELSON) is, in fact, performed by only one reader.6

With the recent grade B recommendation by the US Preventive Task Force for lung cancer screening using low-dose CT imaging, the incidence of pulmonary nodules has the potential to rise dramatically. Because the majority of these nodules are not cancer, volume-based nodule management has been suggested to be more accurate than diameter measurements, with significantly lower false-positive rates. We agree with the call to standardize the performance and interpretation of CT scans to ensure reliable volumetric measurements of pulmonary nodules, thereby improving the ability to predict malignancy.

References

Mehta HJ, Ravenel JG, Shaftman SR, et al. The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules. Chest. 2014;145(3):464-472. [CrossRef] [PubMed]
 
Gavrielides MA, Kinnard LM, Myers KJ, Petrick N. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology. 2009;251(1):26-37. [CrossRef] [PubMed]
 
Goodman LR, Gulsun M, Washington L, Nagy PG, Piacsek KL. Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol. 2006;186(4):989-994. [CrossRef] [PubMed]
 
Gietema HA, Wang Y, Xu D, et al. Pulmonary nodules detected at lung cancer screening: interobserver variability of semiautomated volume measurements. Radiology. 2006;241(1):251-257. [CrossRef] [PubMed]
 
Wang Y, van Klaveren RJ, van der Zaag-Loonen HJ, et al. Effect of nodule characteristics on variability of semiautomated volume measurements in pulmonary nodules detected in a lung cancer screening program. Radiology. 2008;248(2):625-631. [CrossRef] [PubMed]
 
Wang Y, van Klaveren RJ, de Bock GH, et al. No benefit for consensus double reading at baseline screening for lung cancer with the use of semiautomated volumetry software. Radiology. 2012;262(1):320-326. [CrossRef] [PubMed]
 

Figures

Tables

References

Mehta HJ, Ravenel JG, Shaftman SR, et al. The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules. Chest. 2014;145(3):464-472. [CrossRef] [PubMed]
 
Gavrielides MA, Kinnard LM, Myers KJ, Petrick N. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology. 2009;251(1):26-37. [CrossRef] [PubMed]
 
Goodman LR, Gulsun M, Washington L, Nagy PG, Piacsek KL. Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements. AJR Am J Roentgenol. 2006;186(4):989-994. [CrossRef] [PubMed]
 
Gietema HA, Wang Y, Xu D, et al. Pulmonary nodules detected at lung cancer screening: interobserver variability of semiautomated volume measurements. Radiology. 2006;241(1):251-257. [CrossRef] [PubMed]
 
Wang Y, van Klaveren RJ, van der Zaag-Loonen HJ, et al. Effect of nodule characteristics on variability of semiautomated volume measurements in pulmonary nodules detected in a lung cancer screening program. Radiology. 2008;248(2):625-631. [CrossRef] [PubMed]
 
Wang Y, van Klaveren RJ, de Bock GH, et al. No benefit for consensus double reading at baseline screening for lung cancer with the use of semiautomated volumetry software. Radiology. 2012;262(1):320-326. [CrossRef] [PubMed]
 
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