Literature DB >> 24132766

Comparison of three software systems for semi-automatic volumetry of pulmonary nodules on baseline and follow-up CT examinations.

Ying Ru Zhao1, Peter M A van Ooijen2, Monique D Dorrius1, Marjolein Heuvelmans3, Geertruida H de Bock4, Rozemarijn Vliegenthart1, Matthijs Oudkerk3.   

Abstract

BACKGROUND: Early diagnosis of lung cancer in a treatable stage is the main purpose of lung cancer screening by computed tomography (CT). Accurate three-dimensional size and growth measurements are essential to assess the risk of malignancy. Nodule volumes can be calculated by using semi-automated volumetric software. Systematic differences in volume measurements between packages could influence nodule categorization and management decisions.
PURPOSE: To compare volumetric measurements of solid pulmonary nodules on baseline and follow-up CT scans as well as the volume doubling time (VDT) for three software packages.
MATERIAL AND METHODS: From a Lung Cancer Screening study (NELSON), 50 participants were randomly selected from the baseline round. The study population comprised participants with at least one pulmonary nodule at the baseline and consecutive CT examination. The volume of each nodule was determined for both time points using three semi-automated software packages (P1, P2, and P3). Manual modification was performed when automated assessment was visually inaccurate. VDT was calculated to evaluate nodule growth. Volume, VDT, and nodule management were compared for the three software packages, using P1 as the reference standard.
RESULTS: In 25 participants, 147 nodules were present on both examinations (volume: 12.0-436.6 mm(3)). Initial segmentation at baseline was evaluated to be satisfactory in 93.9% of nodules for P1, 84.4 % for P2, and 88.4% for P3. Significant difference was found in measured volume between P1 and the other two packages (P < 0.001). P2 overestimated the volume by 38 ± 24%, and P3 by 50 ± 22%. At baseline, there was consensus on nodule size categorization in 80% for P1&amp;P2 and 74% for P1&amp;P3. At follow-up, consensus on VDT categorization was present in 47% for P1&amp;P2 and 44% for P1&amp;P3.
CONCLUSION: Software packages for lung nodule evaluation yield significant differences in volumetric measurements and VDT. This variation affects the classification of lung nodules, especially in follow-up examinations. © The Foundation Acta Radiologica 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Entities:  

Keywords:  NELSON study; Pulmonary nodule; lung neoplasms; software

Mesh:

Year:  2013        PMID: 24132766     DOI: 10.1177/0284185113508177

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  17 in total

1.  Return of the pulmonary nodule: the radiologist's key role in implementing the 2015 BTS guidelines on the investigation and management of pulmonary nodules.

Authors:  Richard N J Graham; David R Baldwin; Matthew E J Callister; Fergus V Gleeson
Journal:  Br J Radiol       Date:  2016-01-19       Impact factor: 3.039

Review 2.  Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review.

Authors:  Bruno Hochhegger; Matheus Zanon; Stephan Altmayer; Gabriel S Pacini; Fernanda Balbinot; Martina Z Francisco; Ruhana Dalla Costa; Guilherme Watte; Marcel Koenigkam Santos; Marcelo C Barros; Diana Penha; Klaus Irion; Edson Marchiori
Journal:  Lung       Date:  2018-10-09       Impact factor: 2.584

3.  Pulmonary nodules measurements in CT lung cancer screening.

Authors:  Marjolein A Heuvelmans; Matthijs Oudkerk
Journal:  J Thorac Dis       Date:  2018-06       Impact factor: 2.895

4.  Semi-automated pulmonary nodule interval segmentation using the NLST data.

Authors:  Yoganand Balagurunathan; Andrew Beers; Jayashree Kalpathy-Cramer; Michael McNitt-Gray; Lubomir Hadjiiski; Bensheng Zhao; Jiangguo Zhu; Hao Yang; Stephen S F Yip; Hugo J W L Aerts; Sandy Napel; Dmitrii Cherezov; Kenny Cha; Heang-Ping Chan; Carlos Flores; Alberto Garcia; Robert Gillies; Dmitry Goldgof
Journal:  Med Phys       Date:  2018-02-19       Impact factor: 4.071

5.  2D or 3D measurements of pulmonary nodules: preliminary answers and more open questions.

Authors:  Constance de Margerie-Mellon; Benedikt H Heidinger; Alexander A Bankier
Journal:  J Thorac Dis       Date:  2018-02       Impact factor: 2.895

Review 6.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

Review 7.  Volume versus diameter assessment of small pulmonary nodules in CT lung cancer screening.

Authors:  Daiwei Han; Marjolein A Heuvelmans; Matthijs Oudkerk
Journal:  Transl Lung Cancer Res       Date:  2017-02

Review 8.  Clinical utility of quantitative imaging.

Authors:  Andrew B Rosenkrantz; Mishal Mendiratta-Lala; Brian J Bartholmai; Dhakshinamoorthy Ganeshan; Richard G Abramson; Kirsteen R Burton; John-Paul J Yu; Ernest M Scalzetti; Thomas E Yankeelov; Rathan M Subramaniam; Leon Lenchik
Journal:  Acad Radiol       Date:  2014-10-22       Impact factor: 3.173

9.  A Comparison of Lung Nodule Segmentation Algorithms: Methods and Results from a Multi-institutional Study.

Authors:  Jayashree Kalpathy-Cramer; Binsheng Zhao; Dmitry Goldgof; Yuhua Gu; Xingwei Wang; Hao Yang; Yongqiang Tan; Robert Gillies; Sandy Napel
Journal:  J Digit Imaging       Date:  2016-08       Impact factor: 4.056

10.  Evaluation of the 95% limits of agreement of the volumes of 5-year clinically stable solid nodules for the development of a follow-up system for indeterminate solid nodules in CT lung cancer screening.

Authors:  Ryutaro Kakinuma; Yukio Muramatsu; Junta Yamamichi; Shiho Gomi; Estanislao Oubel; Noriyuki Moriyama
Journal:  J Thorac Dis       Date:  2018-01       Impact factor: 2.895

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