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Research ArticleAdult Brain

PACS Integration of Semiautomated Imaging Software Improves Day-to-Day MS Disease Activity Detection

A. Dahan, R. Pereira, C.B. Malpas, T. Kalincik and F. Gaillard
American Journal of Neuroradiology October 2019, 40 (10) 1624-1629; DOI: https://doi.org/10.3174/ajnr.A6195
A. Dahan
aFrom the Department of Radiology (A.D.), Austin Hospital, Heidelberg, Australia
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R. Pereira
bDepartments of Radiology (R.P., F.G.)
dDepartment of Radiology (R.P.), University of Queensland, Brisbane, Queensland, Australia
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C.B. Malpas
cNeurology (T.K., C.M.), Royal Melbourne Hospital, Parkville, Victoria, Australia
eClinical Outcomes Research Unit (CORe) (C.M., T.K.)
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T. Kalincik
cNeurology (T.K., C.M.), Royal Melbourne Hospital, Parkville, Victoria, Australia
eClinical Outcomes Research Unit (CORe) (C.M., T.K.)
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F. Gaillard
bDepartments of Radiology (R.P., F.G.)
fDepartments of Medicine and Radiology (F.G.), University of Melbourne, Melbourne, Australia.
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    Fig 1.

    Software integration into PACS workflow. This flow diagram outlines how the new MR imaging studies for patients with MS are processed by the VisTarsier software in a virtual machine once they are signed off in the radiology information system (RIS) by the radiographer. Successful processing requires all systems to be operational and compatible sequences to be available.

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    Fig 2.

    The proportion of scans showing MS progression within each year. This scatterplot highlights the number of scans and the proportion in which new and enlarging lesions were detected for each study group during each year. The position on the vertical axis corresponds to the proportion of scans showing progression. The position along the horizontal axis corresponds to the study year. The lighter shade corresponds to scans generated with the software.

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    Table 1:

    Demographic and clinical data across each groupa

    ProportionCSSCSoftware-AssistedP Value
    Scans belonging to female patients52/65 (80.00%)599/841 (71.22%).23
    Primary reporting doctors with fellowship certification47/65 (72.31%)586/841 (69.68%).36
    Age at scanMean, 44 yr, 139 daysSD, 11 yr, 223 daysMean, 43 yr, 15 daysSD,11 yr, 256 days.72
    Time since diagnosisMean, 10 yr, 47 daysSD, 6 yr, 230 daysMean, 9 yr, 58 daysSD, 6 yr, 234 days.61
    EDSSMedian, 2.0Quartiles, 25% = 1.9, 75% = 3.1Median, 2.0Quartiles, 25% = 1.0, 75% = 3.5.45
    • ↵a This table summarizes the demographic and clinical details for all eligible patients who underwent an MR imaging brain scan at the Royal Melbourne Hospital from July 1, 2015, until June 30, 2017. χ2 and t test statistics were performed to confirm group similarities.31

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    Table 2:

    Treatment used at scanning for each study groupa

    MedicationCSSC (No.)Software-Assisted (No.)
    Fingolimod35.85% (19)37.30% (282)
    Natalizumab32.08% (17)27.12% (205)
    Dimethyl fumarate3.77% (2)7.80% (59)
    Alemtuzumab1.89% (1)3.44% (26)
    Glatiramer acetate5.66% (3)2.78% (21)
    Interferon β5.66% (3)3.57% (27)
    Otherb9.43% (5)6.61% (50)
    No active treatment5.66% (3)11.38% (86)
    Total100% (53)100% (756)
    Proportion on higher efficacy therapies (fingolimod/natalizumab/alemtuzumab)69.87%67.84%
    • ↵a This table summarizes the number and proportion for each disease-modifying agent at scanning.

    • ↵b Other treatments include teriflunomide, ocrelizumab, rituximab, stem cell transplantation.

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American Journal of Neuroradiology: 40 (10)
American Journal of Neuroradiology
Vol. 40, Issue 10
1 Oct 2019
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Cite this article
A. Dahan, R. Pereira, C.B. Malpas, T. Kalincik, F. Gaillard
PACS Integration of Semiautomated Imaging Software Improves Day-to-Day MS Disease Activity Detection
American Journal of Neuroradiology Oct 2019, 40 (10) 1624-1629; DOI: 10.3174/ajnr.A6195

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PACS Integration of Semiautomated Imaging Software Improves Day-to-Day MS Disease Activity Detection
A. Dahan, R. Pereira, C.B. Malpas, T. Kalincik, F. Gaillard
American Journal of Neuroradiology Oct 2019, 40 (10) 1624-1629; DOI: 10.3174/ajnr.A6195
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