title: Population-based Stroke Atlas for outcome prediction: method and preliminary results for ischemic stroke from CT.
abstract: OBJECTIVE
  Knowledge of outcome prediction is important in stroke management. We propose a lesion size and location-driven method for stroke outcome prediction using a Population-based Stroke Atlas (PSA) linking neurological parameters with neuroimaging in population. The PSA aggregates data from previously treated patients and applies them to currently treated patients. The PSA parameter distribution in the infarct region of a treated patient enables prediction. We introduce a method for PSA calculation, quantify its performance, and use it to illustrate ischemic stroke outcome prediction of modified Rankin Scale (mRS) and Barthel Index (BI).
  
  
  METHODS
  The preliminary PSA was constructed from 128 ischemic stroke cases calculated for 8 variants (various data aggregation schemes) and 3 case selection variables (infarct volume, NIHSS at admission, and NIHSS at day 7), each in 4 ranges. Outcome prediction for 9 parameters (mRS at 7th, and mRS and BI at 30th, 90th, 180th, 360th day) was studied using a leave-one-out approach, requiring 589,824 PSA maps to be analyzed.
  
  
  RESULTS
  Outcomes predicted for different PSA variants are statistically equivalent, so the simplest and most efficient variant aiming at parameter averaging is employed. This variant allows the PSA to be pre-calculated before prediction. The PSA constrained by infarct volume and NIHSS reduces the average prediction error (absolute difference between the predicted and actual values) by a fraction of 0.796; the use of 3 patient-specific variables further lowers it by 0.538. The PSA-based prediction error for mild and severe outcomes (mRS  =  [2]-[5]) is (0.5-0.7). Prediction takes about 8 seconds.
  
  
  CONCLUSIONS
  PSA-based prediction of individual and group mRS and BI scores over time is feasible, fast and simple, but its clinical usefulness requires further studies. The case selection operation improves PSA predictability. A multiplicity of PSAs can be computed independently for different datasets at various centers and easily merged, which enables building powerful PSAs over the community.
authors.affiliation: Biomedical Imaging Lab, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore.
authors.forename: Wieslaw L
authors.initials: WL
authors.lastname: Nowinski
authors.affiliation: Biomedical Imaging Lab, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore.
authors.forename: Varsha
authors.initials: V
authors.lastname: Gupta
authors.affiliation: Biomedical Imaging Lab, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore.
authors.forename: Guoyu
authors.initials: G
authors.lastname: Qian
authors.affiliation: Biomedical Imaging Lab, Singapore Bioimaging Consortium, Agency for Science Technology and Research, Singapore, Singapore; Department of Neurology, Poznan University of Medical Sciences, Poznan, Poland.
authors.forename: Wojciech
authors.initials: W
authors.lastname: Ambrosius
authors.affiliation: Department of Neurology and Cerebrovascular Disorders (L. Bierkowski Hospital), Poznan University of Medical Sciences, Poznan, Poland.
authors.forename: Radoslaw
authors.initials: R
authors.lastname: Kazmierski
mesh_terms: D001921:Brain / Q000473:pathology*; D002545:Brain Ischemia / Q000473:pathology*; D006801:Humans; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods; D017063:Outcome Assessment, Health Care / Q000379:methods; D020521:Stroke / Q000473:pathology*; D014057:Tomography, X-Ray Computed / Q000379:methods