Pulmonary Nodules: Growth Rate Assessment in Patients by Using Serial CT and Three-dimensional Volumetry

Published Online:https://doi.org/10.1148/radiol.11100878

A normative model based on the variability of growth rates measured in lung nodules that were stable for an average of 6.4 years enabled identification of lung cancer.


To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates.

Materials and Methods

This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years ± 1.9 [standard deviation]; range, 2.0–8.5 years). A mean of 4.1 CT examinations per patient ± 1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years ± 0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth.


Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P = .01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P = .05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate–based diagnosis of malignancy was made at a mean of 183 days ± 158, compared with radiologic or clinical diagnosis at 344 days ± 284.


A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.

© RSNA, 2011

Supplemental material: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.11100878/-/DC1


  • 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(5):395–409. Crossref, MedlineGoogle Scholar
  • 2 van Klaveren RJ, Oudkerk M, Prokop M, et al.. Management of lung nodules detected by volume CT scanning. N Engl J Med 2009;361(23):2221–2229. Crossref, MedlineGoogle Scholar
  • 3 Gould MK, Fletcher J, Iannettoni MD, et al.. Evaluation of patients with pulmonary nodules: when is it lung cancer?—ACCP evidence-based clinical practice guidelines (2nd edition). Chest 2007;132(3 suppl):108S–130S. Crossref, MedlineGoogle Scholar
  • 4 Therasse P, Arbuck SG, Eisenhauer EA, et al.. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J Natl Cancer Inst 2000;92(3):205–216. Crossref, MedlineGoogle Scholar
  • 5 MacMahon H, Austin JH, Gamsu G, et al.. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 2005;237(2):395–400. LinkGoogle Scholar
  • 6 Yankelevitz DF, Reeves AP, Kostis WJ, Zhao B, Henschke CI. Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 2000;217(1):251–256. LinkGoogle Scholar
  • 7 Ko JP, Rusinek H, Jacobs EL, et al.. Small pulmonary nodules: volume measurement at chest CT—phantom study. Radiology 2003;228(3):864–870. LinkGoogle Scholar
  • 8 Revel MP, Lefort C, Bissery A, et al.. Pulmonary nodules: preliminary experience with three-dimensional evaluation. Radiology 2004;231(2):459–466. LinkGoogle Scholar
  • 9 Kostis WJ, Reeves AP, Yankelevitz DF, Henschke CI. Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 2003;22(10):1259–1274. Crossref, MedlineGoogle Scholar
  • 10 Kostis WJ, Yankelevitz DF, Reeves AP, Fluture SC, Henschke CI. Small pulmonary nodules: reproducibility of three-dimensional volumetric measurement and estimation of time to follow-up CT. Radiology 2004;231(2):446–452. LinkGoogle Scholar
  • 11 Wormanns D, Kohl G, Klotz E, et al.. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 2004;14(1):86–92. Crossref, MedlineGoogle Scholar
  • 12 Goo JM, Tongdee T, Tongdee R, Yeo K, Hildebolt CF, Bae KT. Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy. Radiology 2005;235(3):850–856. LinkGoogle Scholar
  • 13 Revel MP, Merlin A, Peyrard S, et al.. Software volumetric evaluation of doubling times for differentiating benign versus malignant pulmonary nodules. AJR Am J Roentgenol 2006;187(1):135–142. Crossref, MedlineGoogle Scholar
  • 14 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. LinkGoogle Scholar
  • 15 Das M, Ley-Zaporozhan J, Gietema HA, et al.. Accuracy of automated volumetry of pulmonary nodules across different multislice CT scanners. Eur Radiol 2007;17(8):1979–1984. Crossref, MedlineGoogle Scholar
  • 16 Bolte H, Riedel C, Müller-Hülsbeck S, et al.. Precision of computer-aided volumetry of artificial small solid pulmonary nodules in ex vivo porcine lungs. Br J Radiol 2007;80(954):414–421. Crossref, MedlineGoogle Scholar
  • 17 Gietema HA, Schaefer-Prokop CM, Mali WP, Groenewegen G, Prokop M. Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT— influence of inspiration level, nodule size, and segmentation performance. Radiology 2007;245(3):888–894. LinkGoogle Scholar
  • 18 Nietert PJ, Ravenel JG, Leue WM, et al.. Imprecision in automated volume measurements of pulmonary nodules and its effect on the level of uncertainty in volume doubling time estimation. Chest 2009;135(6):1580–1587. Crossref, MedlineGoogle Scholar
  • 19 Ravenel JG, Leue WM, Nietert PJ, Miller JV, Taylor KK, Silvestri GA. Pulmonary nodule volume: effects of reconstruction parameters on automated measurements—a phantom study. Radiology 2008;247(2):400–408. LinkGoogle Scholar
  • 20 Larici AR, Storto ML, Torge M, et al.. Automated volumetry of pulmonary nodules on multidetector CT: influence of slice thickness, reconstruction algorithm and tube current—preliminary results. Radiol Med (Torino) 2008;113(1):29–42. Crossref, MedlineGoogle Scholar
  • 21 Petrou M, Quint LE, Nan B, Baker LH. Pulmonary nodule volumetric measurement variability as a function of CT slice thickness and nodule morphology. AJR Am J Roentgenol 2007;188(2):306–312. Crossref, MedlineGoogle Scholar
  • 22 Hein PA, Romano VC, Rogalla P, et al.. Variability of semiautomated lung nodule volumetry on ultralow-dose CT: comparison with nodule volumetry on standard-dose CT. J Digit Imaging 2010;23(1):8–17. Crossref, MedlineGoogle Scholar
  • 23 Honda O, Sumikawa H, Johkoh T, et al.. Computer-assisted lung nodule volumetry from multi-detector row CT: influence of image reconstruction parameters. Eur J Radiol 2007;62(1):106–113. Crossref, MedlineGoogle Scholar
  • 24 Das M, Mühlenbruch G, Katoh M, et al.. Automated volumetry of solid pulmonary nodules in a phantom: accuracy across different CT scanner technologies. Invest Radiol 2007;42(5):297–302. Crossref, MedlineGoogle Scholar
  • 25 de Hoop B, Gietema H, van Ginneken B, Zanen P, Groenewegen G, Prokop M. A comparison of six software packages for evaluation of solid lung nodules using semi-automated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 2009;19(4):800–808. Crossref, MedlineGoogle Scholar
  • 26 Marchianò A, Calabrò E, Civelli E, et al.. Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 2009;251(3):919–925. LinkGoogle Scholar
  • 27 Jennings SG, Winer-Muram HT, Tarver RD, Farber MO. Lung tumor growth: assessment with CT–comparison of diameter and cross-sectional area with volume measurements. Radiology 2004;231(3):866–871. LinkGoogle Scholar
  • 28 Tran LN, Brown MS, Goldin JG, et al.. Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography. Acad Radiol 2004;11(12):1355–1360. Crossref, MedlineGoogle Scholar
  • 29 Marten K, Auer F, Schmidt S, Rummeny EJ, Engelke C. Automated CT volumetry of pulmonary metastases: the effect of a reduced growth threshold and target lesion number on the reliability of therapy response assessment using RECIST criteria. Eur Radiol 2007;17(10):2561–2571. Crossref, MedlineGoogle Scholar
  • 30 Revel MP, Bissery A, Bienvenu M, Aycard L, Lefort C, Frija G. Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable? Radiology 2004;231(2):453–458. LinkGoogle Scholar
  • 31 Reeves AP, Biancardi AM, Apanasovich TV, et al.. The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements. Acad Radiol 2007;14(12):1475–1485. Crossref, MedlineGoogle Scholar
  • 32 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, MedlineGoogle Scholar
  • 33 de Hoop B, Gietema H, van de Vorst S, Murphy K, van Klaveren RJ, Prokop M. Pulmonary ground-glass nodules: increase in mass as an early indicator of growth. Radiology 2010;255(1):199–206. LinkGoogle Scholar
  • 34 Mikheev A, Nevsky G, Govindan S, Grossman R, Rusinek H. Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm. J Magn Reson Imaging 2008;27(6):1235–1241. Crossref, MedlineGoogle Scholar
  • 35 Ko JP, Marcus R, Bomsztyk E, et al.. Effect of blood vessels on measurement of nodule volume in a chest phantom. Radiology 2006;239(1):79–85. LinkGoogle Scholar
  • 36 Levene H. Contributions to probability and statistics: essays in honor of Harold Hotelling. 1960; Stanford, Calif: Stanford University Press. Google Scholar
  • 37 Schwartz M. A biomathematical approach to clinical tumor growth. Cancer 1961;14:1272–1294. Crossref, MedlineGoogle Scholar
  • 38 Reeves AP, Chan AB, Yankelevitz DF, Henschke CI, Kressler B, Kostis WJ. On measuring the change in size of pulmonary nodules. IEEE Trans Med Imaging 2006;25(4):435–450. Crossref, MedlineGoogle Scholar
  • 39 Lindell RM, Hartman TE, Swensen SJ, Jett JR, Midthun DE, Mandrekar JN. 5-year lung cancer screening experience: growth curves of 18 lung cancers compared to histologic type, CT attenuation, stage, survival, and size. Chest 2009;136(6):1586–1595. Crossref, MedlineGoogle Scholar
  • 40 Kim HY, Shim YM, Lee KS, Han J, Yi CA, Kim YK. Persistent pulmonary nodular ground-glass opacity at thin-section CT: histopathologic comparisons. Radiology 2007;245(1):267–275. LinkGoogle Scholar
  • 41 Kim TJ, Goo JM, Lee KW, Park CM, Lee HJ. Clinical, pathological and thin-section CT features of persistent multiple ground-glass opacity nodules: comparison with solitary ground-glass opacity nodule. Lung Cancer 2009;64(2):171–178. Crossref, MedlineGoogle Scholar
  • 42 Hasegawa M, Sone S, Takashima S, et al.. Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 2000;73(876):1252–1259. Crossref, MedlineGoogle Scholar
  • 43 Tateishi U, Tsukagoshi S, Inokawa H, Okumura M, Moriyama N. Fluctuation in measurements of pulmonary nodule under tidal volume ventilation on four-dimensional computed tomography: preliminary results. Eur Radiol 2008;18(10):2132–2139. Crossref, MedlineGoogle Scholar
  • 44 Goo JM, Kim KG, Gierada DS, Castro M, Bae KT. Volumetric measurements of lung nodules with multi-detector row CT: effect of changes in lung volume. Korean J Radiol 2006;7(4):243–248. Crossref, MedlineGoogle Scholar
  • 45 Petkovska I, Brown MS, Goldin JG, et al.. The effect of lung volume on nodule size on CT. Acad Radiol 2007;14(4):476–485. Crossref, MedlineGoogle Scholar
  • 46 Naidich DP. Part-solid nodules: two steps forward.... Radiology 2010;255(1):16–18. LinkGoogle Scholar
  • 47 Honda O, Johkoh T, Sumikawa H, et al.. Pulmonary nodules: 3D volumetric measurement with multidetector CT—effect of intravenous contrast medium. Radiology 2007;245(3):881–887. LinkGoogle Scholar
  • 48 Rampinelli C, De Fiori E, Raimondi S, Veronesi G, Bellomi M. In vivo repeatability of automated volume calculations of small pulmonary nodules with CT. AJR Am J Roentgenol 2009;192(6):1657–1661. Crossref, MedlineGoogle Scholar
  • 49 Wang Y, de Bock GH, van Klaveren RJ, et al.. Volumetric measurement of pulmonary nodules at low-dose chest CT: effect of reconstruction setting on measurement variability. Eur Radiol 2010;20(5):1180–1187. Crossref, MedlineGoogle Scholar

Article History

Received April 30, 2010; revision requested June 24; revision received June 13, 2011; accepted July 22; final version accepted August 30.
Published online: Feb 2012
Published in print: Feb 2012