RT Journal Article
SR Electronic
T1 Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 830
OP 833
DO 10.3174/ajnr.A5594
VO 39
IS 5
A1 Nguyen, T.D.
A1 Zhang, S.
A1 Gupta, A.
A1 Zhao, Y.
A1 Gauthier, S.A.
A1 Wang, Y.
YR 2018
UL http://www.ajnr.org/content/39/5/830.abstract
AB SUMMARY: We developed a robust automated algorithm called statistical detection of changes for detecting morphologic changes of multiple sclerosis lesions between 2 T2-weighted FLAIR brain images. Results from 30 patients showed that statistical detection of changes achieved significantly higher sensitivity and specificity (0.964, 95% CI, 0.823–0.994; 0.691, 95% CI, 0.612–0.761) than with the lesion-prediction algorithm (0.614, 95% CI, 0.410–0.784; 0.281, 95% CI, 0.228–0.314), while resulting in a 49% reduction in human review time (P = .007).LPAlesion prediction algorithmSDCstatistical detection of changes