Diagnostic accuracy of semiautomatic lesion detection plus quantitative susceptibility mapping in the identification of new and enhancing multiple sclerosis lesions Academic Article Article uri icon


MeSH Major

  • Alzheimer Disease
  • Brain Mapping
  • Cognition
  • Cognitive Dysfunction
  • Imaging, Three-Dimensional


  • © 2018 The Authors Purpose: To evaluate the diagnostic accuracy of a novel non-contrast brain MRI method based on semiautomatic lesion detection using T2w FLAIR subtraction image, the statistical detection of change (SDC) algorithm (T2w + SDC), and quantitative susceptibility mapping (QSM). This method identifies new lesions and discriminates between enhancing and nonenhancing lesions in multiple sclerosis (MS). Methods: Thirty three MS patients who had MRIs at two different time points with at least one new Gd-enhancing lesion on the 2nd MRI were included in the study. For a reference standard, new lesions were identified by two neuroradiologists on T2w and post-Gd T1w images with the help of T2w + SDC. The diagnostic accuracy of the proposed method based on QSM and T2w + SDC lesion detection (T2w + SDC + QSM) for assessing lesion enhancement status was determined. Receiver operating characteristic (ROC) analysis was performed to compute the optimal lesion susceptibility cutoff value. Results: A total of 165 new lesions (54 enhancing, 111 nonenhancing) were identified. The sensitivity and specificity of T2w + SDC + QSM in predicting lesion enhancement status were 90.7% and 85.6%, respectively. For lesions ≥50 mm 3 , ROC analysis showed an optimal QSM cutoff value of 13.5 ppb with a sensitivity of 88.4% and specificity of 88.6% (0.93, 95% CI, 0.87–0.99). For lesions ≥15 mm 3 , the optimal QSM cutoff was 15.4 ppb with a sensitivity of 77.9% and specificity of 94.0% (0.93, 95% CI, 0.89–0.97). Conclusion: The proposed T2w + SDC + QSM method is highly accurate for identifying and predicting the enhancement status of new MS lesions without the use of Gd injection.

publication date

  • January 2018



  • Academic Article


Digital Object Identifier (DOI)

  • 10.1016/j.nicl.2018.01.013

PubMed ID

  • 29387531

Additional Document Info

start page

  • 143

end page

  • 148


  • 18