title: Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI.
abstract: OBJECTIVE
  Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels.
  
  
  METHODS
  We used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects.
  
  
  RESULTS
  To assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99% (95% CI [97%, 100%]) at the voxel level.
  
  
  CONCLUSIONS
  This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.
authors: Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA. emsweene@jhsph.edu
authors: E M
authors: EM
authors: Sweeney
authors: R T
authors: RT
authors: Shinohara
authors: C D
authors: CD
authors: Shea
authors: D S
authors: DS
authors: Reich
authors: C M
authors: CM
authors: Crainiceanu
mesh_terms: D000328:Adult; D000465:Algorithms; D001927:Brain Diseases / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015994:Incidence; D008137:Longitudinal Studies; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000473:pathology*; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity