Literature DB >> 14615902

Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility.

Dag Wormanns1, Gerhard Kohl, Ernst Klotz, Anke Marheine, Florian Beyer, Walter Heindel, Stefan Diederich.   

Abstract

The aim of this study was to assess the in vivo measurement precision of a software tool for volumetric analysis of pulmonary nodules from two consecutive low-dose multi-row detector CT scans. A total of 151 pulmonary nodules (diameter 2.2-20.5 mm, mean diameter 7.4+/-4.5 mm) in ten subjects with pulmonary metastases were examined with low-dose four-detector-row CT (120 kVp, 20 mAs (effective), collimation 4x1 mm, normalized pitch 1.75, slice thickness 1.25 mm, reconstruction increment 0.8 mm; Somatom VolumeZoom, Siemens). Two consecutive low-dose scans covering the whole lung were performed within 10 min. Nodule volume was determined for all pulmonary nodules visually detected in both scans using the volumetry tool included in the Siemens LungCare software. The 95% limits of agreement between nodule volume measurements on different scans were calculated using the Bland and Altman method for assessing measurement agreement. Intra- and interobserver agreement of volume measurement were determined using repetitive measurements of 50 randomly selected nodules at the same scan by the same and different observers. Taking into account all 151 nodules, 95% limits of agreement were -20.4 to 21.9% (standard error 1.5%); they were -19.3 to 20.4% (standard error 1.7%) for 105 nodules <10 mm. Limits of agreement were -3.9 to 5.7% for intraobserver and -5.5 to 6.6% for interobserver agreement. Precision of in vivo volumetric analysis of nodules with an automatic volumetry software tool was sufficiently high to allow for detection of clinically relevant growth in small pulmonary nodules.

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Year:  2003        PMID: 14615902     DOI: 10.1007/s00330-003-2132-0

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  19 in total

1.  Small pulmonary nodules: volumetrically determined growth rates based on CT evaluation.

Authors:  D F Yankelevitz; A P Reeves; W J Kostis; B Zhao; C I Henschke
Journal:  Radiology       Date:  2000-10       Impact factor: 11.105

2.  Does 2-year stability imply that pulmonary nodules are benign?

Authors:  D F Yankelevitz; C I Henschke
Journal:  AJR Am J Roentgenol       Date:  1997-02       Impact factor: 3.959

3.  Growth rate of small lung cancers detected on mass CT screening.

Authors:  M Hasegawa; S Sone; S Takashima; F Li; Z G Yang; Y Maruyama; T Watanabe
Journal:  Br J Radiol       Date:  2000-12       Impact factor: 3.039

4.  Asymptomatic solitary pulmonary nodules. Host survival, tumor size, and growth rate.

Authors:  J D Steele; P Buell
Journal:  J Thorac Cardiovasc Surg       Date:  1973-01       Impact factor: 5.209

5.  Peripheral measurable bronchogenic carcinoma. Growth rate and period of risk after therapy.

Authors:  W Weiss
Journal:  Am Rev Respir Dis       Date:  1971-02

6.  Statistical methods for assessing agreement between two methods of clinical measurement.

Authors:  J M Bland; D G Altman
Journal:  Lancet       Date:  1986-02-08       Impact factor: 79.321

7.  Early Lung Cancer Action Project: overall design and findings from baseline screening.

Authors:  C I Henschke; D I McCauley; D F Yankelevitz; D P Naidich; G McGuinness; O S Miettinen; D M Libby; M W Pasmantier; J Koizumi; N K Altorki; J P Smith
Journal:  Lancet       Date:  1999-07-10       Impact factor: 79.321

8.  Small pulmonary lesions detected at CT: clinical importance.

Authors:  R F Munden; R D Pugatch; M J Liptay; D J Sugarbaker; L U Le
Journal:  Radiology       Date:  1997-01       Impact factor: 11.105

9.  Screening for early lung cancer with low-dose spiral CT: prevalence in 817 asymptomatic smokers.

Authors:  Stefan Diederich; Dag Wormanns; Michael Semik; Michael Thomas; Horst Lenzen; Nikolaus Roos; Walter Heindel
Journal:  Radiology       Date:  2002-03       Impact factor: 11.105

10.  Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography.

Authors:  M Kaneko; K Eguchi; H Ohmatsu; R Kakinuma; T Naruke; K Suemasu; N Moriyama
Journal:  Radiology       Date:  1996-12       Impact factor: 11.105

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  102 in total

1.  Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists.

Authors:  Katharina Marten; Tobias Seyfarth; Florian Auer; Edzard Wiener; Andreas Grillhösl; Silvia Obenauer; Ernst J Rummeny; Christoph Engelke
Journal:  Eur Radiol       Date:  2004-07-03       Impact factor: 5.315

2.  Evaluating the growth of pulmonary nodular ground-glass opacity on CT: comparison of volume rendering and thin slice images.

Authors:  Mingzhu Liang; Xueguo Liu; Weidong Li; Kunwei Li; Xiangmeng Chen; Guojie Wang; Kai Chen; Jinxin Zhang
Journal:  J Huazhong Univ Sci Technolog Med Sci       Date:  2011-12-16

3.  Application of CT-PSF-based computer-simulated lung nodules for evaluating the accuracy of computer-aided volumetry.

Authors:  Ayumu Funaki; Masaki Ohkubo; Shinichi Wada; Kohei Murao; Toru Matsumoto; Shinji Niizuma
Journal:  Radiol Phys Technol       Date:  2012-03-25

Review 4.  Screening for lung cancer using low-dose spiral CT: 10 years later, state of the art.

Authors:  M Zompatori; M Mascalchi; F Ciccarese; N Sverzellati; U Pastorino
Journal:  Radiol Med       Date:  2012-06-28       Impact factor: 3.469

5.  Zone of transition: a potential source of error in tumor volume estimation.

Authors:  Lijuan Zhang; David F Yankelevitz; Claudia I Henschke; Artit C Jirapatnakul; Anthony P Reeves; Darryl Carter
Journal:  Radiology       Date:  2010-08       Impact factor: 11.105

6.  The influence of initial outlines on manual segmentation.

Authors:  William F Sensakovic; Adam Starkey; Rachael Roberts; Christopher Straus; Philip Caligiuri; Masha Kocherginsky; Samuel G Armato
Journal:  Med Phys       Date:  2010-05       Impact factor: 4.071

7.  Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography.

Authors:  G Foti; N Faccioli; M D'Onofrio; A Contro; T Milazzo; R Pozzi Mucelli
Journal:  Radiol Med       Date:  2010-06-23       Impact factor: 3.469

8.  Semi-automated volumetric analysis of lymph node metastases during follow-up--initial results.

Authors:  Michael Fabel; H Bolte; H von Tengg-Kobligk; L Bornemann; V Dicken; S Delorme; H-U Kauczor; M Heller; J Biederer
Journal:  Eur Radiol       Date:  2010-10-17       Impact factor: 5.315

9.  Solitary pulmonary nodule: detection and management.

Authors:  S Diederich; M Das
Journal:  Cancer Imaging       Date:  2006-10-31       Impact factor: 3.909

10.  Treatment response classification of liver metastatic disease evaluated on imaging. Are RECIST unidimensional measurements accurate?

Authors:  Michael Mantatzis; Stylianos Kakolyris; Kyriakos Amarantidis; Anastasios Karayiannakis; Panos Prassopoulos
Journal:  Eur Radiol       Date:  2009-02-24       Impact factor: 5.315

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