Literature DB >> 10478251

Brain tumor volume measurement: comparison of manual and semiautomated methods.

B N Joe1, M B Fukui, C C Meltzer, Q S Huang, R S Day, P J Greer, M E Bozik.   

Abstract

PURPOSE: To compare the reliability of two approaches to measuring enhancing brain tumor volumes--the conventional manual trace method and a threshold-based, semiautomated computer software method.
MATERIALS AND METHODS: Two operators rated contrast material-enhanced, T1-weighted axial magnetic resonance (MR) image data sets from 16 patients aged 21-71 years with high-grade gliomas. Each MR data set was rated twice by using manual tracing and twice by using the semiautomated method. The semiautomated measurement method involved a thresholding algorithm based on mixture modeling. The data collection time for each method was recorded. Reliability was measured by using inter- and intraoperator agreement indexes.
RESULTS: Mean intraoperator agreement indexes (+/- SD) were 0.90 +/- 0.09 (operator 1) and 0.83 +/- 0.15 (operator 2) for the manual trace method and 0.83 +/- 0.17 (operator 1) and 0.84 +/- 0.16 (operator 2) for the semiautomated measurement method. The mean interoperator agreement was 0.85 +/- 0.14 for the manual method and 0.82 +/- 0.18 for the semiautomated method. The semiautomated method was faster than the manual trace method by an average of 4.6 minutes per patient.
CONCLUSION: The semiautomated computer method of measuring tumor volume was faster than the manual trace method. Semiautomated computer approaches offer an alternative to manual tracing for measuring serial tumor volumes in patients with high-grade brain neoplasms.

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Year:  1999        PMID: 10478251     DOI: 10.1148/radiology.212.3.r99se22811

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


  26 in total

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Journal:  Neuroradiology       Date:  2010-06-19       Impact factor: 2.804

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Authors:  Bennett A Landman; Andrew J Asman; Andrew G Scoggins; John A Bogovic; Joshua A Stein; Jerry L Prince
Journal:  Neuroimage       Date:  2011-08-02       Impact factor: 6.556

4.  Extraction of metastatic lymph nodes from MR images using two deformable model-based approaches.

Authors:  Jia-Yin Zhou; Wen Fang; Kap-Luk Chan; Vincent F H Chong; James B K Khoo
Journal:  J Digit Imaging       Date:  2007-12       Impact factor: 4.056

5.  COLLABORATIVE LABELING OF MALIGNANT GLIOMA.

Authors:  Zhoubing Xu; Andrew J Asman; Eesha Singh; Lola Chambless; Reid Thompson; Bennett A Landman
Journal:  Proc IEEE Int Symp Biomed Imaging       Date:  2012-12-31

6.  Rough-fuzzy clustering and unsupervised feature selection for wavelet based MR image segmentation.

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7.  Simultaneous Segmentation and Statistical Label Fusion.

Authors:  Andrew J Asman; Bennett A Landmana
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2012-02-23

8.  Segmentation of malignant gliomas through remote collaboration and statistical fusion.

Authors:  Zhoubing Xu; Andrew J Asman; Eesha Singh; Lola Chambless; Reid Thompson; Bennett A Landman
Journal:  Med Phys       Date:  2012-10       Impact factor: 4.071

9.  Radiogenomics correlation between MR imaging features and major genetic profiles in glioblastoma.

Authors:  Eun Kyoung Hong; Seung Hong Choi; Dong Jae Shin; Sang Won Jo; Roh-Eul Yoo; Koung Mi Kang; Tae Jin Yun; Ji-Hoon Kim; Chul-Ho Sohn; Sung-Hye Park; Jae-Kyung Won; Tae Min Kim; Chul-Kee Park; Il Han Kim; Soon Tae Lee
Journal:  Eur Radiol       Date:  2018-05-02       Impact factor: 5.315

10.  Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

Authors:  Jingxin Nie; Zhong Xue; Tianming Liu; Geoffrey S Young; Kian Setayesh; Lei Guo; Stephen T C Wong
Journal:  Comput Med Imaging Graph       Date:  2009-05-14       Impact factor: 4.790

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