Literature DB >> 23859235

Multiple sclerosis: identification of temporal changes in brain lesions with computer-assisted detection software.

M Bilello1, M Arkuszewski, P Nucifora, I Nasrallah, E R Melhem, L Cirillo, J Krejza.   

Abstract

Multiple sclerosis (MS) is a chronic disease with a progressing and evolving course. Serial imaging with MRI is the mainstay in monitoring and managing MS patients. In this work we demonstrate the performance of a locally developed computer-assisted detection (CAD) software used to track temporal changes in brain MS lesions. CAD tracks changes in T2-bright MS lesions between two time points on a 3D high-resolution isotropic FLAIR MR sequence of the brain acquired at 3 Tesla. The program consists of an image-processing pipeline, and displays scrollable difference maps used as an aid to the neuroradiologist for assessing lesional change. To assess the value of the software we have compared diagnostic accuracy and duration of interpretation of the CAD-assisted and routine clinical interpretations in 98 randomly chosen, paired MR examinations from 88 patients (68 women, 20 men, mean age 43.5, age range 21-75) with a diagnosis of definite MS. The ground truth was determined by a three-expert panel. In case-wise analysis, CAD interpretation showed higher sensitivity than a clinical report (87% vs 77%, respectively). Lesion-wise analysis demonstrated improved sensitivity of CAD over a routine clinical interpretation of 40%-48%. Mean software-assisted interpretation time was 2.7 min. Our study demonstrates the potential of including CAD software in the workflow of neuroradiology practice for the detection of MS lesional change. Automated quantification of temporal change in MS lesion load may also be used in clinical research, e.g., in drug trials.

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Year:  2013        PMID: 23859235      PMCID: PMC5228721          DOI: 10.1177/197140091302600202

Source DB:  PubMed          Journal:  Neuroradiol J        ISSN: 1971-4009


  20 in total

1.  Multiple Sclerosis Lesions in the Brain: Computer-Assisted Assessment of Lesion Load Dynamics on 3D FLAIR MR Images.

Authors:  M Bilello; M Arkuszewski; I Nasrallah; X Wu; G Erus; J Krejza
Journal:  Neuroradiol J       Date:  2012-03-01

Review 2.  Multiple sclerosis: new insights and trends.

Authors:  M Inglese
Journal:  AJNR Am J Neuroradiol       Date:  2006-05       Impact factor: 3.825

3.  Standardized MR imaging protocol for multiple sclerosis: Consortium of MS Centers consensus guidelines.

Authors:  J H Simon; D Li; A Traboulsee; P K Coyle; D L Arnold; F Barkhof; J A Frank; R Grossman; D W Paty; E W Radue; J S Wolinsky
Journal:  AJNR Am J Neuroradiol       Date:  2006-02       Impact factor: 3.825

4.  Evaluating intensity normalization on MRIs of human brain with multiple sclerosis.

Authors:  Mohak Shah; Yiming Xiao; Nagesh Subbanna; Simon Francis; Douglas L Arnold; D Louis Collins; Tal Arbel
Journal:  Med Image Anal       Date:  2010-12-25       Impact factor: 8.545

5.  Intra- and inter-observer agreement of brain MRI lesion volume measurements in multiple sclerosis. A comparison of techniques.

Authors:  M Filippi; M A Horsfield; S Bressi; V Martinelli; C Baratti; P Reganati; A Campi; D H Miller; G Comi
Journal:  Brain       Date:  1995-12       Impact factor: 13.501

6.  CT colonography and computer-aided detection: effect of false-positive results on reader specificity and reading efficiency in a low-prevalence screening population.

Authors:  Stuart A Taylor; Rebecca Greenhalgh; Rajapandian Ilangovan; Emily Tam; Vikram A Sahni; David Burling; Jie Zhang; Paul Bassett; Perry J Pickhardt; Steve Halligan
Journal:  Radiology       Date:  2008-02-21       Impact factor: 11.105

7.  CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software.

Authors:  Stuart A Taylor; Susan C Charman; Philippe Lefere; Elizabeth G McFarland; Erik K Paulson; Judy Yee; Rizwan Aslam; John M Barlow; Arun Gupta; David H Kim; Chad M Miller; Steve Halligan
Journal:  Radiology       Date:  2007-12-19       Impact factor: 11.105

8.  Subtraction MR images in a multiple sclerosis multicenter clinical trial setting.

Authors:  Bastiaan Moraal; Dominik S Meier; Peter A Poppe; Jeroen J G Geurts; Hugo Vrenken; William M A Jonker; Dirk L Knol; Ronald A van Schijndel; Petra J W Pouwels; Christoph Pohl; Lars Bauer; Rupert Sandbrink; Charles R G Guttmann; Frederik Barkhof
Journal:  Radiology       Date:  2008-11-26       Impact factor: 11.105

9.  Imaging of inflammatory lesions at 3.0 Tesla in patients with clinically isolated syndromes suggestive of multiple sclerosis: a comparison of fluid-attenuated inversion recovery with T2 turbo spin-echo.

Authors:  Mike P Wattjes; Götz G Lutterbey; Michael Harzheim; Jürgen Gieseke; Frank Träber; Luisa Klotz; Thomas Klockgether; Hans H Schild
Journal:  Eur Radiol       Date:  2006-04-04       Impact factor: 5.315

10.  A novel method for automatic determination of different stages of multiple sclerosis lesions in brain MR FLAIR images.

Authors:  Rasoul Khayati; Mansur Vafadust; Farzad Towhidkhah; S Massood Nabavi
Journal:  Comput Med Imaging Graph       Date:  2007-12-04       Impact factor: 4.790

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  3 in total

Review 1.  Artificial intelligence for precision education in radiology.

Authors:  Michael Tran Duong; Andreas M Rauschecker; Jeffrey D Rudie; Po-Hao Chen; Tessa S Cook; R Nick Bryan; Suyash Mohan
Journal:  Br J Radiol       Date:  2019-07-26       Impact factor: 3.039

2.  Incorporation of image data from a previous examination in 3D serial MR imaging.

Authors:  Guobin Li; Jürgen Hennig; Esther Raithel; Martin Büchert; Dominik Paul; Jan G Korvink; Maxim Zaitsev
Journal:  MAGMA       Date:  2015-01-09       Impact factor: 2.310

3.  Automated lesion detection on MRI scans using combined unsupervised and supervised methods.

Authors:  Dazhou Guo; Julius Fridriksson; Paul Fillmore; Christopher Rorden; Hongkai Yu; Kang Zheng; Song Wang
Journal:  BMC Med Imaging       Date:  2015-10-30       Impact factor: 1.930

  3 in total

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