Literature DB >> 14519875

Effect of varying CT section width on volumetric measurement of lung tumors and application of compensatory equations.

Helen T Winer-Muram1, S Gregory Jennings, Cristopher A Meyer, Yun Liang, Alex M Aisen, Robert D Tarver, Ronald C McGarry.   

Abstract

PURPOSE: To determine how volume measurements of simulated and clinical lung tumors at standard computed tomographic (CT) lung window and level settings vary with section width and to derive and apply compensatory equations.
MATERIALS AND METHODS: Spherical simulated tumors of varying diameters were imaged with varying CT section widths, the images were displayed on a workstation, the cross-sectional area of the tumor on each section was measured by using elliptical and perimeter methods, and the areas were integrated to compute tumor volume. The actual and measured tumor volumes for differing section widths and tumor diameters were compared, and compensatory equations were derived. The equations were applied to contemporaneous chest CT images obtained in patients with stage I lung cancer, and the difference between thick- and thin-section-derived volumes before and after application of the equations was determined.
RESULTS: All simulated tumor volumes were overestimated 11%-278%; overestimation varied directly with section width and inversely with tumor diameter. With both measurement methods, mean thin-section volumes of clinical tumors in 55 patients were significantly smaller (P <.01) than mean thick-section volumes: Mean elliptical measurements were 15,025 mm3 (thin) and 18,037 mm3 (thick), with a 20.0% difference; mean perimeter measurements were 16,164 mm3 (thin) and 20,718 mm3 (thick), with a 22.2% difference. The thin-section-to-thick-section volume difference was larger for the smallest tumors. Thin-section volumes were smaller than thick-section volumes in 53 patients with the elliptical method and in 51 patients with the perimeter method. Applying the equations decreased the difference between thick- and thin-section volumes in 37 (67%) of the 55 patients with the elliptical method and in 41 (74%) patients with the perimeter method. The mean thin-section-to-thick-section volume difference became nonsignificant with the perimeter method but remained significant with the elliptical method.
CONCLUSION: Measured lung tumor volumes vary significantly with varying CT section width; overestimation varies directly with section width and inversely with tumor size. Compensatory equations that are somewhat effective in reducing these effects can be derived. Copyright RSNA, 2003

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Year:  2003        PMID: 14519875     DOI: 10.1148/radiol.2291020859

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  32 in total

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2.  Effect of CT scanning parameters on volumetric measurements of pulmonary nodules by 3D active contour segmentation: a phantom study.

Authors:  Ted W Way; Heang-Ping Chan; Mitchell M Goodsitt; Berkman Sahiner; Lubomir M Hadjiiski; Chuan Zhou; Aamer Chughtai
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Review 3.  Noncalcified lung nodules: volumetric assessment with thoracic CT.

Authors:  Marios A Gavrielides; Lisa M Kinnard; Kyle J Myers; Nicholas Petrick
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4.  Imprecision in automated volume measurements of pulmonary nodules and its effect on the level of uncertainty in volume doubling time estimation.

Authors:  Paul J Nietert; James G Ravenel; William M Leue; James V Miller; Katherine K Taylor; Elizabeth S Garrett-Mayer; Gerard A Silvestri
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5.  Variability of semiautomated lung nodule volumetry on ultralow-dose CT: comparison with nodule volumetry on standard-dose CT.

Authors:  Patrick A Hein; Valentina C Romano; Patrik Rogalla; Christian Klessen; Alexander Lembcke; Lars Bornemann; Volker Dicken; Bernd Hamm; Hans-Christian Bauknecht
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6.  Pulmonary nodule volume: effects of reconstruction parameters on automated measurements--a phantom study.

Authors:  James G Ravenel; William M Leue; Paul J Nietert; James V Miller; Katherine K Taylor; Gerard A Silvestri
Journal:  Radiology       Date:  2008-05       Impact factor: 11.105

7.  Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

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8.  Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer.

Authors:  Binsheng Zhao; Leonard P James; Chaya S Moskowitz; Pingzhen Guo; Michelle S Ginsberg; Robert A Lefkowitz; Yilin Qin; Gregory J Riely; Mark G Kris; Lawrence H Schwartz
Journal:  Radiology       Date:  2009-07       Impact factor: 11.105

9.  Exploring Variability in CT Characterization of Tumors: A Preliminary Phantom Study.

Authors:  Binsheng Zhao; Yongqiang Tan; Wei Yann Tsai; Lawrence H Schwartz; Lin Lu
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

10.  Computed tomography assessment of response to therapy: tumor volume change measurement, truth data, and error.

Authors:  Michael F McNitt-Gray; Luc M Bidaut; Samuel G Armato; Charles R Meyer; Marios A Gavrielides; Charles Fenimore; Geoffrey McLennan; Nicholas Petrick; Binsheng Zhao; Anthony P Reeves; Reinhard Beichel; Hyun-Jung Grace Kim; Lisa Kinnard
Journal:  Transl Oncol       Date:  2009-12       Impact factor: 4.243

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