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Published in: European Radiology 8/2010

Open Access 01-08-2010 | Computed Tomography

Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably

Authors: H. Ashraf, B. de Hoop, S. B. Shaker, A. Dirksen, K. S. Bach, H. Hansen, M. Prokop, J. H. Pedersen

Published in: European Radiology | Issue 8/2010

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Abstract

Objective

We examined the reproducibility of lung nodule volumetry software that offers three different volumetry algorithms.

Methods

In a lung cancer screening trial, 188 baseline nodules >5 mm were identified. Including follow-ups, these nodules formed a study-set of 545 nodules. Nodules were independently double read by two readers using commercially available volumetry software. The software offers readers three different analysing algorithms. We compared the inter-observer variability of nodule volumetry when the readers used the same and different algorithms.

Results

Both readers were able to correctly segment and measure 72% of nodules. In 80% of these cases, the readers chose the same algorithm. When readers used the same algorithm, exactly the same volume was measured in 50% of readings and a difference of >25% was observed in 4%. When the readers used different algorithms, 83% of measurements showed a difference of >25%.

Conclusion

Modern volumetric software failed to correctly segment a high number of screen detected nodules. While choosing a different algorithm can yield better segmentation of a lung nodule, reproducibility of volumetric measurements deteriorates substantially when different algorithms were used. It is crucial even in the same software package to choose identical parameters for follow-up.
Literature
1.
go back to reference Verschakelen JA, Bogaert J, de Wever W (2002) Computed tomography in staging for lung cancer. Eur Respir J 19:40–48CrossRef Verschakelen JA, Bogaert J, de Wever W (2002) Computed tomography in staging for lung cancer. Eur Respir J 19:40–48CrossRef
2.
go back to reference Henschke CI, McCauley DI, Yankelevitz DF et al (1999) Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354:99–105CrossRefPubMed Henschke CI, McCauley DI, Yankelevitz DF et al (1999) Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354:99–105CrossRefPubMed
3.
go back to reference Swensen SJ, Jett JR, Hartman TE et al (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226:756–761CrossRefPubMed Swensen SJ, Jett JR, Hartman TE et al (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226:756–761CrossRefPubMed
4.
go back to reference Xu DM, van der Zaag-Loonen HJ, Oudkerk M et al (2009) Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology 250:264–272CrossRefPubMed Xu DM, van der Zaag-Loonen HJ, Oudkerk M et al (2009) Smooth or attached solid indeterminate nodules detected at baseline CT screening in the NELSON study: cancer risk during 1 year of follow-up. Radiology 250:264–272CrossRefPubMed
5.
go back to reference Hasegawa M, Sone S, Takashima S et al (2000) Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 73:1252–1259PubMed Hasegawa M, Sone S, Takashima S et al (2000) Growth rate of small lung cancers detected on mass CT screening. Br J Radiol 73:1252–1259PubMed
6.
go back to reference Pauls S, Kürschner C, Dharaiya E et al (2008) Comparison of manual and automated size measurements of lung metastases on MDCT images: potential influence on therapeutic decisions. Eur J Radiol 66:19–26CrossRefPubMed Pauls S, Kürschner C, Dharaiya E et al (2008) Comparison of manual and automated size measurements of lung metastases on MDCT images: potential influence on therapeutic decisions. Eur J Radiol 66:19–26CrossRefPubMed
7.
go back to reference Kostis WJ, Reeves AP, Yankelevitz DF et al (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22:1259–1274CrossRefPubMed Kostis WJ, Reeves AP, Yankelevitz DF et al (2003) Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical CT images. IEEE Trans Med Imaging 22:1259–1274CrossRefPubMed
8.
go back to reference Yankelevitz DF, Anthony PR, William JK, MS et al (2000) Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217:251–256 Yankelevitz DF, Anthony PR, William JK, MS et al (2000) Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation. Radiology 217:251–256
9.
go back to reference Xu D, Gietema H, de Koning H et al (2006) Nodule management protocol of the NELSON randomised lung cancer screening trial. Lung Cancer 54:177–184CrossRefPubMed Xu D, Gietema H, de Koning H et al (2006) Nodule management protocol of the NELSON randomised lung cancer screening trial. Lung Cancer 54:177–184CrossRefPubMed
10.
go back to reference De Hoop B, Gietema HA, van Ginneken B et al (2009) 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 19:800–808CrossRefPubMed De Hoop B, Gietema HA, van Ginneken B et al (2009) 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 19:800–808CrossRefPubMed
12.
go back to reference Pedersen JH, Ashraf H, Dirksen A et al (2009) The Danish randomized lung cancer CT screening trial—overall design and results of the prevalence round. J Thorac Oncol 4:608–614CrossRefPubMed Pedersen JH, Ashraf H, Dirksen A et al (2009) The Danish randomized lung cancer CT screening trial—overall design and results of the prevalence round. J Thorac Oncol 4:608–614CrossRefPubMed
13.
go back to reference Gietema HA, Wang Y, Xu D et al (2006) Pulmonary nodules detected at lung cancer screening: interobserver variability of semiautomated volume measurements. Radiology 241:251–256CrossRefPubMed Gietema HA, Wang Y, Xu D et al (2006) Pulmonary nodules detected at lung cancer screening: interobserver variability of semiautomated volume measurements. Radiology 241:251–256CrossRefPubMed
14.
go back to reference Gietema HA, Schaefer-Prokop CM, Mali WPTM et al (2007) Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT—influence of inspiration level, nodule size, and segmentation performance. Radiology 245:888–894CrossRefPubMed Gietema HA, Schaefer-Prokop CM, Mali WPTM et al (2007) Pulmonary nodules: interscan variability of semiautomated volume measurements with multisection CT—influence of inspiration level, nodule size, and segmentation performance. Radiology 245:888–894CrossRefPubMed
15.
go back to reference Wang Y, van Klaveren RJ, van der Zaag-Loonen HJ et al (2008) Effect of nodule characteristics on variability of semiautomated volume measurements in pulmonary nodules detected in a lung cancer screening program. Radiology 248:625–631CrossRefPubMed Wang Y, van Klaveren RJ, van der Zaag-Loonen HJ et al (2008) Effect of nodule characteristics on variability of semiautomated volume measurements in pulmonary nodules detected in a lung cancer screening program. Radiology 248:625–631CrossRefPubMed
16.
go back to reference Marchiano A, Calabro E, Civelli E et al (2009) Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 251:919–925CrossRefPubMed Marchiano A, Calabro E, Civelli E et al (2009) Pulmonary nodules: volume repeatability at multidetector CT lung cancer screening. Radiology 251:919–925CrossRefPubMed
17.
go back to reference Rampinelli C, Fiori DE, Raimondi S et al (2009) In vivo repeatability of automated volume calculations of small pulmonary nodules with CT. AJR Am J Roentgenol 192:1657–1661CrossRefPubMed Rampinelli C, Fiori DE, Raimondi S et al (2009) In vivo repeatability of automated volume calculations of small pulmonary nodules with CT. AJR Am J Roentgenol 192:1657–1661CrossRefPubMed
18.
go back to reference Gurung J, Maataoui A, Khan M et al (2006) Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. Rofo 178:71–77PubMed Gurung J, Maataoui A, Khan M et al (2006) Automated detection of lung nodules in multidetector CT: influence of different reconstruction protocols on performance of a software prototype. Rofo 178:71–77PubMed
Metadata
Title
Lung nodule volumetry: segmentation algorithms within the same software package cannot be used interchangeably
Authors
H. Ashraf
B. de Hoop
S. B. Shaker
A. Dirksen
K. S. Bach
H. Hansen
M. Prokop
J. H. Pedersen
Publication date
01-08-2010
Publisher
Springer-Verlag
Published in
European Radiology / Issue 8/2010
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-010-1749-z

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