Literature DB >> 19457831

In vivo repeatability of automated volume calculations of small pulmonary nodules with CT.

Cristiano Rampinelli1, Elvio De Fiori, Sara Raimondi, Giulia Veronesi, Massimo Bellomi.   

Abstract

OBJECTIVE: The objectives of our study were to evaluate the in vivo reproducibility of automated volume calculations of small lung nodules with both low-dose and standard-dose CT and to assess whether repeatability within each technique varies according to the diameter, site, or morphology of the nodule or to percentage of emphysema. SUBJECTS AND METHODS: Sixty-six subjects with 83 solid pulmonary nodules between 5 and 10 mm in diameter were enrolled in this prospective study. Four consecutive MDCT data sets, two low dose and two standard dose, were obtained for each nodule on separate breath-holds during the same session. The volume of each nodule was calculated by automated software. Repeatability was evaluated by Bland-Altman's approach and the coefficient of repeatability. Associations of the percentage of volume variation between two measurements with nodule diameter, emphysema percentage, nodule site, and nodule morphology were assessed by Spearman's correlation coefficient and the Kruskal-Wallis test. A p value of < 0.05 was considered statistically significant.
RESULTS: The range of variation of the volumes of pulmonary nodules between two subsequent measurements was -38% +/- 60% for low-dose CT and -27% +/- 40% for standard-dose CT. No significant statistical association was found between variation in volume measurements and nodule site, nodule diameter, nodule morphology, or emphysema percentage by semiautomated calculation of lung density.
CONCLUSION: Automated volume calculations of small pulmonary nodules can significantly differ between two subsequent breath-holds with both low-dose and standard-dose CT techniques; in clinical practice we recommend that a volume variation of greater than 30% for nodules between 5 and 10 mm should be confirmed by follow-up CT to be sure that a nodule is actually growing.

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Year:  2009        PMID: 19457831     DOI: 10.2214/AJR.08.1825

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  9 in total

1.  Inter- and intrascanner variability of pulmonary nodule volumetry on low-dose 64-row CT: an anthropomorphic phantom study.

Authors:  X Xie; M J Willemink; Y Zhao; P A de Jong; P M A van Ooijen; M Oudkerk; M J W Greuter; R Vliegenthart
Journal:  Br J Radiol       Date:  2013-07-24       Impact factor: 3.039

2.  Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods.

Authors:  Stefano Young; Hyun J Grace Kim; Moe Moe Ko; War War Ko; Carlos Flores; Michael F McNitt-Gray
Journal:  Med Phys       Date:  2015-05       Impact factor: 4.071

3.  Indeterminate pulmonary nodules: risk for having or for developing lung cancer?

Authors:  Pierre P Massion; Ronald C Walker
Journal:  Cancer Prev Res (Phila)       Date:  2014-10-27

4.  Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry.

Authors:  Jane P Ko; Erika J Berman; Manmeen Kaur; James S Babb; Elan Bomsztyk; Alissa K Greenberg; David P Naidich; Henry Rusinek
Journal:  Radiology       Date:  2011-12-09       Impact factor: 11.105

5.  Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study.

Authors:  Ryoji Mikayama; Takashi Shirasaka; Tsukasa Kojima; Yuki Sakai; Hidetake Yabuuchi; Masatoshi Kondo; Toyoyuki Kato
Journal:  Br J Radiol       Date:  2021-12-15       Impact factor: 3.039

6.  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
Journal:  Eur Radiol       Date:  2010-03-20       Impact factor: 5.315

7.  Reduction in growth threshold for pulmonary metastases: an opportunity for volumetry and its impact on treatment decisions.

Authors:  M N Vogel; S Schmücker; O Maksimovic; J Hartmann; C D Claussen; M Horger
Journal:  Br J Radiol       Date:  2012-07       Impact factor: 3.039

8.  Evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules.

Authors:  Ming-Yue Wu; Yong Li; Bin-Jie Fu; Guo-Shu Wang; Zhi-Gang Chu; Dan Deng
Journal:  J Appl Clin Med Phys       Date:  2020-12-24       Impact factor: 2.102

Review 9.  Lung cancer screening update.

Authors:  Massimo Bellomi; Cristiano Rampinelli; Elvio De Fiori; Lorenzo Preda; Giulia Veronesi
Journal:  Cancer Imaging       Date:  2009-10-02       Impact factor: 3.909

  9 in total

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