Literature DB >> 8700036

The variability of manual and computer assisted quantification of multiple sclerosis lesion volumes.

J R Mitchell1, S J Karlik, D H Lee, M Eliasziw, G P Rice, A Fenster.   

Abstract

The high resolution and excellent soft tissue contrast of Magnetic Resonance Imaging (MRI) have enabled direct, noninvasive visualization of Multiple Sclerosis (MS) lesions in vivo. This has allowed the quantification of changes in the appearance of lesions in MR exams to be used as a measure of disease state. Nevertheless, accurate quantification techniques are subject to inter- and intra-operator variability, which may hinder monitoring of disease progression. We have developed a computer program to assist an experienced operator in the quantification of MS lesions in standard spin-echo MR exams. The accuracy of assisted and manual quantification under known conditions was studied using exams of a test phantom, while inter- and intra-operator reliability and variability were studied using exams of a MS patient. Results from the phantom study show that accuracy is improved by assisted quantification. The patient exam results indicate that assisted quantification reduced inter-operator variability from 0.34 to 0.17 cm3, and reduced intra-operator variability from 0.23 to 0.15 cm3. In addition, the minimum significant change between two successive measurements of lesion volume by the same operator was 0.64 cm3 for manual quantification and 0.42 cm3 for assisted quantification. For two different operators making successive measurements, the minimum significant change was 0.94 cm3 for manual quantification, but only 0.47 cm3 for assisted quantification. Finally, the number of lesions to be monitored for an average change in volume at a given power and significance level was reduced by a factor of 2-4 by assisted quantification. These results suggest that assisted quantification may have practical applications in clinical trials, especially those that are large, multicenter, or extended over time, and therefore require lesion measurements by one or more operators.

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Year:  1996        PMID: 8700036     DOI: 10.1118/1.597685

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  5 in total

1.  Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy.

Authors:  I Fotina; C Lütgendorf-Caucig; M Stock; R Pötter; D Georg
Journal:  Strahlenther Onkol       Date:  2012-01-27       Impact factor: 3.621

Review 2.  Assessing observer variability: a user's guide.

Authors:  Zoran B Popović; James D Thomas
Journal:  Cardiovasc Diagn Ther       Date:  2017-06

Review 3.  Tracking cerebral white matter changes across the lifespan: insights from diffusion tensor imaging studies.

Authors:  Qian Jun Yap; Irvin Teh; Paolo Fusar-Poli; Min Yi Sum; Carissa Kuswanto; Kang Sim
Journal:  J Neural Transm (Vienna)       Date:  2013-01-18       Impact factor: 3.575

4.  A letter to the editor regarding the mini review "Assessing observer variability: a user's guide".

Authors:  Nidhal Bouchahda
Journal:  Cardiovasc Diagn Ther       Date:  2022-02

5.  Deep neural network to locate and segment brain tumors outperformed the expert technicians who created the training data.

Authors:  Joseph Ross Mitchell; Konstantinos Kamnitsas; Kyle W Singleton; Scott A Whitmire; Kamala R Clark-Swanson; Sara Ranjbar; Cassandra R Rickertsen; Sandra K Johnston; Kathleen M Egan; Dana E Rollison; John Arrington; Karl N Krecke; Theodore J Passe; Jared T Verdoorn; Alex A Nagelschneider; Carrie M Carr; John D Port; Alice Patton; Norbert G Campeau; Greta B Liebo; Laurence J Eckel; Christopher P Wood; Christopher H Hunt; Prasanna Vibhute; Kent D Nelson; Joseph M Hoxworth; Ameet C Patel; Brian W Chong; Jeffrey S Ross; Jerrold L Boxerman; Michael A Vogelbaum; Leland S Hu; Ben Glocker; Kristin R Swanson
Journal:  J Med Imaging (Bellingham)       Date:  2020-10-16
  5 in total

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