title: Prediction of stroke thrombolysis outcome using CT brain machine learning.
abstract: A critical decision-step in the emergency treatment of ischemic stroke is whether or not to administer thrombolysis - a treatment that can result in good recovery, or deterioration due to symptomatic intracranial haemorrhage (SICH). Certain imaging features based upon early computerized tomography (CT), in combination with clinical variables, have been found to predict SICH, albeit with modest accuracy. In this proof-of-concept study, we determine whether machine learning of CT images can predict which patients receiving tPA will develop SICH as opposed to showing clinical improvement with no haemorrhage. Clinical records and CT brains of 116 acute ischemic stroke patients treated with intravenous thrombolysis were collected retrospectively (including 16 who developed SICH). The sample was split into training (n = 106) and test sets (n = 10), repeatedly for 1760 different combinations. CT brain images acted as inputs into a support vector machine (SVM), along with clinical severity. Performance of the SVM was compared with established prognostication tools (SEDAN and HAT scores; original, or after adaptation to our cohort). Predictive performance, assessed as area under receiver-operating-characteristic curve (AUC), of the SVM (0.744) compared favourably with that of prognostic scores (original and adapted versions: 0.626-0.720; p < 0.01). The SVM also identified 9 out of 16 SICHs, as opposed to 1-5 using prognostic scores, assuming a 10% SICH frequency (p < 0.001). In summary, machine learning methods applied to acute stroke CT images offer automation, and potentially improved performance, for prediction of SICH following thrombolysis. Larger-scale cohorts, and incorporation of advanced imaging, should be tested with such methods.
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Bentley
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Jeban
authors.initials: J
authors.lastname: Ganesalingam
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Anoma Lalani
authors.initials: AL
authors.lastname: Carlton Jones
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Kate
authors.initials: K
authors.lastname: Mahady
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Sarah
authors.initials: S
authors.lastname: Epton
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Paul
authors.initials: P
authors.lastname: Rinne
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Pankaj
authors.initials: P
authors.lastname: Sharma
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Omid
authors.initials: O
authors.lastname: Halse
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Amrish
authors.initials: A
authors.lastname: Mehta
authors.affiliation: Division of Brain Sciences, Imperial College London, Charing Cross Hospital Campus, Fulham Palace Rd., London W6 8RF, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D019540:Area Under Curve; D001185:Artificial Intelligence; D001921:Brain / Q000000981:diagnostic imaging* / Q000187:drug effects; D005260:Female; D005343:Fibrinolytic Agents / Q000627:therapeutic use*; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D012720:Severity of Illness Index; D020521:Stroke / Q000188:drug therapy*; D010959:Tissue Plasminogen Activator / Q000627:therapeutic use*; D014057:Tomography, X-Ray Computed
chemical_list: D005343:Fibrinolytic Agents; D010959:Tissue Plasminogen Activator
keywords: Imaging; Machine learning; Prediction; Stroke; Thrombolysis