Acknowledgement
This study was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (NRF-2016R1A2B4012155 and NRF-2018R1D1A1B07049989). This study was supported by a Korea University Ansan Hospital Grant (O1801231, O1700681). This study was supported by Dong-Kook Pharmaceutical.
References
- National Lung Screening Trial Research Team, Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395-409 https://doi.org/10.1056/NEJMoa1102873
- MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology 2017;284:228-243 https://doi.org/10.1148/radiol.2017161659
- Vardhanabhuti V, Loader RJ, Mitchell GR, Riordan RD, Roobottom CA. Image quality assessment of standard- and low-dose chest CT using filtered back projection, adaptive statistical iterative reconstruction, and novel model-based iterative reconstruction algorithms. AJR Am J Roentgenol 2013;200:545-552 https://doi.org/10.2214/AJR.12.9424
- Katsura M, Matsuda I, Akahane M, Sato J, Akai H, Yasaka K, et al. Model-based iterative reconstruction technique for radiation dose reduction in chest CT: comparison with the adaptive statistical iterative reconstruction technique. Eur Radiol 2012;22:1613-1623 https://doi.org/10.1007/s00330-012-2452-z
- Yuki H, Oda S, Utsunomiya D, Funama Y, Kidoh M, Namimoto T, et al. Clinical impact of model-based type iterative reconstruction with fast reconstruction time on image quality of low-dose screening chest CT. Acta Radiol 2016;57:295-302 https://doi.org/10.1177/0284185115575537
- Kim C, Lee KY, Shin C, Kang EY, Oh YW, Ha M, et al. Comparison of filtered back projection, hybrid iterative reconstruction, model-based iterative reconstruction, and virtual monoenergetic reconstruction images at both low- and standard-dose settings in measurement of emphysema volume and airway wall thickness: a CT phantom study. Korean J Radiol 2018;19:809-817 https://doi.org/10.3348/kjr.2018.19.4.809
- Gavrielides MA, Berman BP, Supanich M, Schultz K, Li Q, Petrick N, et al. Quantitative assessment of nonsolid pulmonary nodule volume with computed tomography in a phantom study. Quant Imaging Med Surg 2017;7:623-635 https://doi.org/10.21037/qims.2017.12.07
- Maruyama S, Fukushima Y, Miyamae Y, Koizumi K. Usefulness of model-based iterative reconstruction in semi-automatic volumetry for ground-glass nodules at ultra-low-dose CT: a phantom study. Radiol Phys Technol 2018;11:235-241 https://doi.org/10.1007/s12194-018-0442-9
- Chen B, Barnhart H, Richard S, Robins M, Colsher J, Samei E. Volumetric quantification of lung nodules in CT with iterative reconstruction (ASiR and MBIR). Med Phys 2013;40:111902 https://doi.org/10.1118/1.4823463
- Hasegawa M, Sone S, Takashima S, Li F, Yang ZG, Maruyama Y, et al. Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 2000;73:1252-1259 https://doi.org/10.1259/bjr.73.876.11205667
- 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;231:866-871 https://doi.org/10.1148/radiol.2313030715
- Kim C, Lee SM, Choe J, Chae EJ, Do KH, Seo JB. Volume doubling time of lung cancer detected in idiopathic interstitial pneumonia: comparison with that in chronic obstructive pulmonary disease. Eur Radiol 2018;28:1402-1409 https://doi.org/10.1007/s00330-017-5091-6
- Devaraj A, van Ginneken B, Nair A, Baldwin D. Use of volumetry for lung nodule management: theory and practice. Radiology 2017;284:630-644 https://doi.org/10.1148/radiol.2017151022
- Callister MEJ, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J on behalf of the British Thoracic Society Standards of Care Committee et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 2015;70 Suppl 2:ii1-ii54 https://doi.org/10.1136/thoraxjnl-2015-207168
- Cohen JG, Reymond E, Lederlin M, Medici M, Lantuejoul S, Laurent F, et al. Differentiating pre- and minimally invasive from invasive adenocarcinoma using CT-features in persistent pulmonary part-solid nodules in Caucasian patients. Eur J Radiol 2015;84:738-744 https://doi.org/10.1016/j.ejrad.2014.12.031
- Cicchetti DV. Guidelines, criteria, and rules of thumb for evaluating normed and standardized assessment instruments in psychology. Psychological Assessment 1994;6:284-290 https://doi.org/10.1037/1040-3590.6.4.284
- Kim H, Park CM, Song YS, Lee SM, Goo JM. Influence of radiation dose and iterative reconstruction algorithms for measurement accuracy and reproducibility of pulmonary nodule volumetry: a phantom study. Eur J Radiol 2014;83:848-857 https://doi.org/10.1016/j.ejrad.2014.01.025
- Kim H, Park CM, Chae HD, Lee SM, Goo JM. Impact of radiation dose and iterative reconstruction on pulmonary nodule measurements at chest CT: a phantom study. Diagn Interv Radiol 2015;21:459-465 https://doi.org/10.5152/dir.2015.14541
- Cohen JG, Kim H, Park SB, van Ginneken B, Ferretti GR, Lee CH, et al. Comparison of the effects of model-based iterative reconstruction and filtered back projection algorithms on software measurements in pulmonary subsolid nodules. Eur Radiol 2017;27:3266-3274 https://doi.org/10.1007/s00330-016-4716-5
- Doo KW, Kang EY, Yong HS, Woo OH, Lee KY, Oh YW. Accuracy of lung nodule volumetry in low-dose CT with iterative reconstruction: an anthropomorphic thoracic phantom study. Br J Radiol 2014;87:20130644 https://doi.org/10.1259/bjr.20130644
- Willemink MJ, de Jong PA, Leiner T, de Heer LM, Nievelstein RA, Budde RP, et al. Iterative reconstruction techniques for computed tomography Part 1: technical principles. Eur Radiol 2013;23:1623-1631 https://doi.org/10.1007/s00330-012-2765-y
- Shikuma K, Menju T, Chen F, Kubo T, Muro S, Sumiyoshi S, et al. Is volumetric 3-dimensional computed tomography useful to predict histological tumour invasiveness? Analysis of 211 lesions of cT1N0M0 lung adenocarcinoma. Interact Cardiovasc Thorac Surg 2016;22:831-838 https://doi.org/10.1093/icvts/ivw037
- Kamiya S, Iwano S, Umakoshi H, Ito R, Shimamoto H, Nakamura S, et al. Computer-aided volumetry of part-solid lung cancers by using CT: solid component size predicts prognosis. Radiology 2018;287:1030-1040
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