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Follow-up MRI in multiple sclerosis patients: automated co-registration and lesion color-coding improves diagnostic accuracy and reduces reading time

  • David ZopfsEmail author
  • Kai R. Laukamp
  • Stefanie Paquet
  • Simon Lennartz
  • Daniel Pinto dos Santos
  • Christoph Kabbasch
  • Alexander Bunck
  • Marc Schlamann
  • Jan Borggrefe
Neuro

Abstract

Objectives

In multiple sclerosis (MS), the heterogeneous and numerous appearances of lesions may impair diagnostic accuracy. This study investigates if a combined automated co-registration and lesion color-coding method (AC) improves assessment of MS follow-up MRI compared with conventional reading (CR).

Methods

We retrospectively assessed 70 follow-up MRI of 53 patients. Heterogeneous datasets of diverse scanners and institutions were used. Two readers determined presence of (a) progression, (b) regression, (c) mixed change, or (d) stable disease between the two examinations using corresponding FLAIR sequences in CR and AC-assisted reading. Consensus reference reading was provided by two blinded radiologists. Kappa statistics tested interrater agreement, McNemar’s test dichotomous variables, and Wilcoxon’s test continuous variables (statistical significance p ≤ 0.05).

Results

The cohort comprised 41 female and 12 male patients with a mean age of 40 (± 14) years. Average rating time was reduced from 78 (± 36) to 44 (±22) s with the AC approach (p < 0.001). The time needed to start and match datasets with AC was 14 (± 1) s. Compared with CR, AC improved interrater agreement, both between raters (0.52 vs. 0.67) and between raters and consensus reference reading (0.47/0.5 vs. 0.83/0.78). Compared with CR, the diagnostic accuracy increased from 67 to 90% (reader 1, p < 0.01) and from 70 to 87% (reader 2, p < 0.05) in the AC-assisted reading.

Conclusions

Compared with CR, automated co-registration and lesion color-coding of MS-associated FLAIR-lesions in follow-up MRI increased diagnostic accuracy and reduced the time required for follow-up evaluation significantly. The AC algorithm therefore appears to be helpful to improve MS follow-up assessments in clinical routine.

Key Points

Automated co-registration and lesion color-coding increases diagnostic accuracy in the assessment of MRI follow-up examinations in patients with multiple sclerosis.

Automated co-registration and lesion color-coding reduces reading time of MRI follow-up examinations in patients with multiple sclerosis.

Automated co-registration and lesion color-coding improved interrater agreement in the assessment of MRI follow-up examinations in patients with multiple sclerosis.

Keywords

Multiple sclerosis Magnetic resonance imaging Disease progression Brain Image processing, computer-assisted 

Abbreviations

AC

Automated co-registration and lesion color-coding image reading approach

CR

Conventional reading

FLAIR

Fluid-attenuated inversion recovery

MRI

Magnetic resonance imaging

MS

Multiple sclerosis

PACS

Picture archiving and communication system

RIS

Radiological information system

SD

Standard deviation.

Notes

Funding

The authors state that this work has not received any funding.

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Jan Borggrefe.

Conflict of interest

The authors of this manuscript declare relationships with the following companies:

Jan Borggrefe received speaker honoraria from Philips.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Performed at one institution

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Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  • David Zopfs
    • 1
    Email author
  • Kai R. Laukamp
    • 1
    • 2
    • 3
  • Stefanie Paquet
    • 1
  • Simon Lennartz
    • 1
  • Daniel Pinto dos Santos
    • 1
  • Christoph Kabbasch
    • 1
  • Alexander Bunck
    • 1
  • Marc Schlamann
    • 1
  • Jan Borggrefe
    • 1
  1. 1.Faculty of Medicine and University Hospital Cologne, Institute for Diagnostic and Interventional RadiologyUniversity of CologneCologneGermany
  2. 2.Department of RadiologyUniversity Hospitals Cleveland Medical CenterClevelandUSA
  3. 3.Department of RadiologyCase Western Reserve UniversityClevelandUSA

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