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An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions.

J D Dworkin1, K A Linn2, I Oguz3

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A new method accurately counts distinct multiple sclerosis lesions, even when overlapping. This lesion count, combined with lesion size, better predicts disability than traditional lesion load alone.

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Area of Science:

  • Neurology
  • Radiology
  • Biostatistics

Background:

  • Lesion load in multiple sclerosis (MS) has a weak link to clinical outcomes.
  • Assessing lesion count is challenging in MS due to confluent lesions.
  • A novel statistical technique is introduced for counting distinct MS lesions.

Purpose of the Study:

  • To develop and validate a new method for counting pathologically distinct lesions in MS using cross-sectional MRI data.
  • To assess the correlation between the novel lesion count and established biomarkers.
  • To determine the association of lesion count and size with clinical disability in MS patients.

Main Methods:

  • Utilized MR imaging to map lesion probabilities.
  • Quantified map texture using a novel technique to count lesion clusters.
  • Validated the method against a criterion standard in 60 subjects and assessed reliability with 14 scans.

Main Results:

  • The novel lesion count strongly correlated with the criterion standard (r=0.97, P<.001).
  • Variability of the novel count was comparable to lesion load.
  • Lesion count, adjusted for lesion load and age, negatively associated with Expanded Disability Status Scale (t58=-2.73, P<.01).
  • Average lesion size showed stronger association with disability (r=0.35, P<.01) than lesion load or count alone.

Conclusions:

  • Introduced a novel technique for counting distinct MS lesions from cross-sectional MRI.
  • The method recovers obscured longitudinal information and improves lesion size estimation.
  • Accurate lesion size estimation, derived from the new count, correlates better with MS disability than lesion load or count alone.