Multiple Sclerosis: Identification of Temporal Changes in Brain Lesions with Computer-Assisted Detection Software

被引:15
作者
Bilello, M. [1 ]
Arkuszewski, M. [1 ,2 ]
Nucifora, P. [1 ]
Nasrallah, I. [1 ]
Melhem, E. R. [3 ]
Cirillo, L. [4 ]
Krejza, J. [3 ,5 ]
机构
[1] Univ Penn, Dept Radiol, Div Neuroradiol, Philadelphia, PA USA
[2] Med Univ Silesia, Dept Neurol, Katowice, Poland
[3] Univ Maryland, Sch Med, Dept Diagnost Radiol & Nucl Med, Baltimore, MD USA
[4] Univ Bologna, Dept Radiol, Bologna, Italy
[5] Al Imam Muhammad Ibn Saud Islam Univ, Riyadh, Saudi Arabia
关键词
multiple sclerosis; brain lesions; magnetic resonance; imaging; computer assessment;
D O I
10.1177/197140091302600202
中图分类号
R445 [影像诊断学];
学科分类号
100207 ;
摘要
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.
引用
收藏
页码:143 / 150
页数:8
相关论文
共 20 条
  • [1] Automatic segmentation of gadolinium-enhanced multiple sclerosis lesions
    Bedell, BJ
    Narayana, PA
    [J]. MAGNETIC RESONANCE IN MEDICINE, 1998, 39 (06) : 935 - 940
  • [2] Bilello M, 2012, NEURORADIOL J, V2, P412
  • [3] Computer-aided lung nodule detection in CT: Results of large-scale observer test
    Brown, MS
    Goldin, JG
    Rogers, S
    Kim, HJ
    Suh, RD
    McNitt-Gray, MF
    Shah, SK
    Truong, D
    Brown, K
    Sayre, JW
    Gjertson, DW
    Batra, P
    Aberle, DR
    [J]. ACADEMIC RADIOLOGY, 2005, 12 (06) : 681 - 686
  • [4] Elliott C, 2010, LECT NOTES COMPUT SC, V6362, P290
  • [5] Intra- and inter-observer agreement of brain MRI lesion volume measurements in multiple sclerosis - A comparison of techniques
    Filippi, M
    Horsfield, MA
    Bressi, S
    Martinelli, V
    Baratti, C
    Reganati, P
    Campi, A
    Miller, DH
    Comi, G
    [J]. BRAIN, 1995, 118 : 1593 - 1600
  • [6] Multiple sclerosis lesion detect in the brain: A comparison of fast fluid-attenuated inversion recovery and conventional T2-weighted dual spin echo
    GawneCain, ML
    ORiordan, JI
    Thompson, AJ
    Moseley, IF
    Miller, DH
    [J]. NEUROLOGY, 1997, 49 (02) : 364 - 370
  • [7] Automated detection and characterization of multiple sclerosis lesions in brain MR images
    Goldberg-Zimring, D
    Achiron, A
    Miron, S
    Faibel, M
    Azhari, H
    [J]. MAGNETIC RESONANCE IMAGING, 1998, 16 (03) : 311 - 318
  • [8] Inglese M, 2006, AM J NEURORADIOL, V27, P954
  • [9] Automatic Detection of Gadolinium-Enhancing Multiple Sclerosis Lesions in Brain MRI Using Conditional Random Fields
    Karimaghaloo, Zahra
    Shah, Mohak
    Francis, Simon J.
    Arnold, Douglas L.
    Collins, D. Louis
    Arbel, Tal
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2012, 31 (06) : 1181 - 1194
  • [10] Karimaghaloo Z, 2010, LECT NOTES COMPUT SC, V6363, P41