TY - JOUR
T1 - Region-specific agreement in ASPECTS estimation between neuroradiologists and e-ASPECTS software
AU - Neuhaus, Ain
AU - Seyedsaadat, Seyed Mohammad
AU - Mihal, David
AU - Benson, John
AU - Mark, Ian
AU - Kallmes, David F.
AU - Brinjikji, Waleed
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Background and purpose The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used measure of ischemic change on non-contrast CT. Although predictive of long-term outcome, ASPECTS is limited by its modest interobserver agreement. One potential solution to this is the use of machine learning strategies, such as e-ASPECTS, to detect ischemia. Here, we compared e-ASPECTS with manual scoring by experienced neuroradiologists for all 10 individual ASPECTS regions. Materials and methods We retrospectively reviewed 178 baseline non-contrast CT scans from patients with acute ischemic stroke undergoing endovascular thrombectomy. All scans were reviewed by two independent neuroradiologists with a third reader arbitrating disagreements for a consensus read. Each ASPECTS region was scored individually. All scans were then evaluated using a machine learning-based software package (e-ASPECTS, Brainomix). Interobserver agreement between readers and the software for each region was calculated with a kappa statistic. Results The median ASPECTS was 9 for manual scoring and 8.5 for e-ASPECTS, with an overall agreement of κ=0.248. Regional agreement varied from κ=0.094 (M1) to κ=0.555 (lentiform), with better performance in subcortical regions. When corrected for the low number of infarcts in any given region, prevalence-adjusted bias-adjusted kappa ranged from 0.483 (insula) to 0.888 (M3), with greater agreement for cortical areas. Intraclass correlation coefficients were between 0.09 (M1) and 0.556 (lentiform). Conclusion Manual scoring and e-ASPECTS had fair agreement in our dataset on a per-region basis. This warrants further investigation using follow-up scans or MRI as the gold standard measure of true ASPECTS.
AB - Background and purpose The Alberta Stroke Program Early CT Score (ASPECTS) is a widely used measure of ischemic change on non-contrast CT. Although predictive of long-term outcome, ASPECTS is limited by its modest interobserver agreement. One potential solution to this is the use of machine learning strategies, such as e-ASPECTS, to detect ischemia. Here, we compared e-ASPECTS with manual scoring by experienced neuroradiologists for all 10 individual ASPECTS regions. Materials and methods We retrospectively reviewed 178 baseline non-contrast CT scans from patients with acute ischemic stroke undergoing endovascular thrombectomy. All scans were reviewed by two independent neuroradiologists with a third reader arbitrating disagreements for a consensus read. Each ASPECTS region was scored individually. All scans were then evaluated using a machine learning-based software package (e-ASPECTS, Brainomix). Interobserver agreement between readers and the software for each region was calculated with a kappa statistic. Results The median ASPECTS was 9 for manual scoring and 8.5 for e-ASPECTS, with an overall agreement of κ=0.248. Regional agreement varied from κ=0.094 (M1) to κ=0.555 (lentiform), with better performance in subcortical regions. When corrected for the low number of infarcts in any given region, prevalence-adjusted bias-adjusted kappa ranged from 0.483 (insula) to 0.888 (M3), with greater agreement for cortical areas. Intraclass correlation coefficients were between 0.09 (M1) and 0.556 (lentiform). Conclusion Manual scoring and e-ASPECTS had fair agreement in our dataset on a per-region basis. This warrants further investigation using follow-up scans or MRI as the gold standard measure of true ASPECTS.
KW - CT
KW - stroke
KW - thrombectomy
UR - http://www.scopus.com/inward/record.url?scp=85076453559&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076453559&partnerID=8YFLogxK
U2 - 10.1136/neurintsurg-2019-015442
DO - 10.1136/neurintsurg-2019-015442
M3 - Article
C2 - 31818971
AN - SCOPUS:85076453559
SN - 1759-8478
VL - 12
SP - 720
EP - 723
JO - Journal of neurointerventional surgery
JF - Journal of neurointerventional surgery
IS - 7
ER -