Current perspectives for the size measurement of screening-detected lung nodules

Current perspectives for the size measurement of screening-detected lung nodules

Hyungjin Kim1,2, Chang Min Park1,2,3

1Department of Radiology, Seoul National University Hospital, Seoul, Korea; 2Department of Radiology, Seoul National University College of Medicine, Seoul, Korea; 3Cancer Research Institute, Seoul National University, Seoul, Korea

Correspondence to: Chang Min Park, MD, PhD. Department of Radiology, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, Korea. Email:

Provenance: This is an invited Editorial commissioned by the Section Editor Jun Zhou (Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, China).

Comment on: Heuvelmans MA, Walter JE, Vliegenthart R, et al. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax 2017. [Epub ahead of print].

Submitted Jan 25, 2018. Accepted for publication Feb 13, 2018.

doi: 10.21037/jtd.2018.03.24

Lung cancer is the leading cause of cancer-related mortality worldwide. National Lung Screening Trial (NLST) demonstrated reduced lung cancer mortality of 20% for whom underwent annual low-dose CT screening (1). For the screening-detected nodules, size measurement is mandatory as the nodule size is directly proportional to the lung cancer risk (2). For the size estimation, uni-dimensional or bi-dimensional diameter measurement have been the standard in practice. Diameter measurement is easy, practical, and has been adopted as the standard method in the clinical practice guidelines for the management of lung nodules (3-6).

The Dutch-Belgian lung cancer screening trial (NELSON) was the first screening program that the management was guided by the volumetric lung nodule measurement instead of the manual diameter measurement. In NELSON, a commercial software (LungCARE) was used for the semi-automated volumetry for the solid nodules and this approach led to high negative predictive values (99.7% in first round, 99.9% in second round) and presumably fewer false-positive results than in other lung cancer screening trials (7,8). Importantly, volume-based management protocol yielded sensitivity and specificity of 90.9% and 94.9% for the 2-year lung cancer probability (2). Given the high specificity of the volume-based protocol, the authors suggested that the lung cancer screening should be done using volumetric software (2).

Heuvelmans et al. (9) recently reported on the disagreement of diameter and volume measurements for the size estimation using 2,240 solid nodules (volume, 50–500 mm3) from NELSON. Diameter–based volume calculation, either the maximum or mean axial diameter, led to the overestimation of nodule volume (47.2–85.1%) compared to the volumetry software-derived volume. Mean overestimation of volume based on the diameter measurements was higher for the nodules with volume range 200–500 mm3 and with non-smooth margin. In addition, they demonstrated that the diameter measurement was less sensitive for the size-based risk stratification and that it could not reflect the true dimension of the nodules given the substantial diameter variation within a nodule (median, 2.8 mm; interquartile range, 2.2–3.7 mm). Intranodular diameter variation exceeded the suggested cutoff in screening guidelines such as Lung-RADS (1.5 mm). This indicated that the nodule’s interval growth could not be reliably detected based on the diameter measurement. Actually, these results advocated the use of semi-automated volumetry in the lung CT screening trial. The strength of this study was that the nodules were extracted from a large, multicenter, randomized screening trial, which is potentially identical to the target population, and that the nodules of intermediate size were included in the analysis. Another interesting point was that this study proved the intrinsic limitation of uni- or bi-dimensional measurement excluding the human variation caused by the manual measurement.

The two axes of measurement are accuracy and reproducibility. Studies to date have reported the strength of semi-automated nodule volumetry in both aspects of the measurements. For the measurement accuracy, Xie et al. (10) reported that the volume measurement was more accurate with the semi-automated volumetry than the manual measurement in a phantom study, although both methods underestimated the actual nodule volume (underestimation, 26.4% for manual measurement vs. 7.6% for semi-automated method). Other experimental studies with simulated lung nodules (solid or subsolid) also showed promising measurement accuracy for the semi-automated volumetry (11-13). With respect to the measurement reproducibility, which may gain greater significance in the clinical scenarios, the variability range of nodule volume was reported to be generally ±25% (14,15). There has been massive investigation into the inherent variability of nodule volume measurement on CT scans in terms of the patient factors, reader factors and CT scanning factors (16,17). The measurement variability range refers to the cutoff for the determination of true change and thus small measurement variation may enable early detection of the lung cancer at the follow-up CT scans. The measurement accuracy and reproducibility can be translated into the diagnostic accuracy and reproducibility (18,19). Accurate and reliable risk stratification is a prerequisite of the screening programs as the management decision is based on the risk categories. Thus, the semi-automated volumetry is potentially more favorable than the diameter measurement.

In addition, volumetric nodule segmentation can provide additional information other than the simple dimensional data. Volume doubling time can be calculated based on the follow-up scans, as was used in the NELSON trial (2). Nodule attenuation and mass can be obtained in case of subsolid nodules (20). Furthermore, computer-aided radiomics analysis can be performed based on the three-dimensional segmentation profile. A recent study demonstrated an add value of image feature analysis for the diagnosis of lung cancer in small nodules (4–20 mm) in a sized matched case-control study using NLST population (21).

For the implementation of semi-automated volumetry in the lung cancer screening programs, a few issues have to be addressed. First, nodule segmentation performance is largely dependent on the segmentation algorithm used. There are volumetry software programs capable of subsolid nodule segmentation (12,22), although not all programs perform equally well. Subsolid nodules are identified in approximately 5% of the baseline CT screening (23,24) and have high malignant potential if they persist at the follow-up scans. In addition, solid portion in the part-solid nodule is regarded as pathologic invasive component and is the key for the clinical decision-making (25). Therefore, adequate segmentation of the whole nodule as well as its internal solid portion should be guaranteed. Second, juxta-pleural and juxta-vascular nodules are less likely to be segmented satisfactorily. These nodules may be handled by the manual measurement. Third, a quality-controlled standardized CT scanning protocol is absolutely imperative. Fourth, prospective comparison between the volumetric and diameter measurements is required in the clinical trial-basis as there is little evidence to date. Evaluation for the lung cancer diagnosis as well as its impact on the prognosis should be scrutinized. Lastly, more evidences on the performance of semi-automated volumetry for the small screening-detected nodules should be cumulated. Data on the screening population are mostly from the NELSON trial. Thus, more clinical data should accrue from other trials in order to generalize the use of semi-automated volumetry in the screening programs.

In conclusion, the potential benefit and strength of the semi-automated volumetry in the management of the screening-detected nodules have been emphasized by the data from NELSON trial. Nevertheless, care should be taken for the implementation of the semi-automated volumetry in the screening programs as there are many obstacles to be solved currently.




Conflicts of Interest: The authors have no conflicts of interest to declare.


  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. [Crossref] [PubMed]
  2. Horeweg N, van Rosmalen J, Heuvelmans MA, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol 2014;15:1332-41. [Crossref] [PubMed]
  3. Callister ME, Baldwin DR, Akram AR, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 2015;70 Suppl 2:ii1-54. [Crossref] [PubMed]
  4. 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-120S.
  5. National Comprehensive Cancer Network. NCCN clinical practice guidelines in oncology lung cancer screening version 2.2014.
  6. American College of Radiology. Lung CT Screening Reporting & Data System version 1.0. Available online: Accessed 21 Jan 2018.
  7. Lee H, Um SW. Probability of lung cancer based on the size threshold and volume-doubling time for lung nodules detected in low-dose CT screening. Ann Transl Med 2015;3:21. [PubMed]
  8. van Klaveren RJ, Oudkerk M, Prokop M, et al. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009;361:2221-9. [Crossref] [PubMed]
  9. Heuvelmans MA, Walter JE, Vliegenthart R, et al. Disagreement of diameter and volume measurements for pulmonary nodule size estimation in CT lung cancer screening. Thorax 2017. [Epub ahead of print]. [PubMed]
  10. Xie X, Zhao Y, Snijder RA, et al. Sensitivity and accuracy of volumetry of pulmonary nodules on low-dose 16- and 64-row multi-detector CT: an anthropomorphic phantom study. Eur Radiol 2013;23:139-47. [Crossref] [PubMed]
  11. Doo KW, Kang EY, Yong HS, et al. Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study. Br J Radiol 2014;87:20130644. [Crossref] [PubMed]
  12. Kim H, Park CM, Lee SM, et al. A comparison of two commercial volumetry software programs in the analysis of pulmonary ground-glass nodules: segmentation capability and measurement accuracy. Korean J Radiol 2013;14:683-91. [Crossref] [PubMed]
  13. Siegelman JW, Supanich MP, Gavrielides MA. Pulmonary nodules with ground-glass opacity can be reliably measured with low-dose techniques regardless of iterative reconstruction: results of a phantom study. AJR Am J Roentgenol 2015;204:1242-7. [Crossref] [PubMed]
  14. Gietema HA, Schaefer-Prokop CM, Mali WP, et al. Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT- influence of inspiration level, nodule size, and segmentation performance. Radiology 2007;245:888-94. [Crossref] [PubMed]
  15. Marchiano A, Calabro E, Civelli E, et al. Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 2009;251:919-25. [Crossref] [PubMed]
  16. Devaraj A, van Ginneken B, Nair A, et al. Use of volumetry for lung nodule management: theory and practice. Radiology 2017;284:630-44. [Crossref] [PubMed]
  17. Gavrielides MA, Kinnard LM, Myers KJ, et al. Noncalcified lung nodules: volumetric assessment with thoracic CT. Radiology 2009;251:26-37. [Crossref] [PubMed]
  18. Kim H, Park CM, Hwang EJ, et al. Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement. Eur Radiol 2017. [Epub ahead of print]. [Crossref] [PubMed]
  19. Jeon KN, Goo JM, Lee CH, et al. Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography. Invest Radiol 2012;47:457-61. [Crossref] [PubMed]
  20. Kim H, Park CM, Woo S, et al. Pure and part-solid pulmonary ground-glass nodules: measurement variability of volume and mass in nodules with a solid portion less than or equal to 5 mm. Radiology 2013;269:585-93. [Crossref] [PubMed]
  21. Huang P, Park S, Yan R, et al. Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: a matched case-control study. Radiology 2018;286:286-95. [Crossref] [PubMed]
  22. Cohen JG, Goo JM, Yoo RE, et al. Software performance in segmenting ground-glass and solid components of subsolid nodules in pulmonary adenocarcinomas. Eur Radiol 2016;26:4465-74. [Crossref] [PubMed]
  23. Henschke CI, Yip R, Smith JP, et al. CT screening for lung cancer: part-solid nodules in baseline and annual repeat rounds. AJR Am J Roentgenol 2016;207:1176-84. [Crossref] [PubMed]
  24. Yankelevitz DF, Yip R, Smith JP, et al. CT screening for lung cancer: nonsolid nodules in baseline and annual repeat rounds. Radiology 2015;277:555-64. [Crossref] [PubMed]
  25. Ko JP, Suh J, Ibidapo O, et al. Lung adenocarcinoma: correlation of quantitative CT findings with pathologic findings. Radiology 2016;280:931-9. [Crossref] [PubMed]
Cite this article as: Kim H, Park CM. Current perspectives for the size measurement of screening-detected lung nodules. J Thorac Dis 2018;10(3):1242-1244. doi: 10.21037/jtd.2018.03.24