Imperial College London
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DrPaulBentley

Faculty of Medicine, Department of Brain Sciences

Senior Clinical Research Fellow
 
 
 
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Contact

 

p.bentley

 
 
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Location

 

10L21Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bentley:2014:10.1016/j.nicl.2014.02.003,
author = {Bentley, P and Ganesalingam, J and Jones, ALC and Mahady, K and Epton, S and Rinne, P and Sharma, P and Halse, O and Mehta, A and Rueckert, D},
doi = {10.1016/j.nicl.2014.02.003},
journal = {NeuroImage: Clinical},
pages = {635--640},
title = {Prediction of stroke thrombolysis outcome using CT brain machine learning},
url = {http://dx.doi.org/10.1016/j.nicl.2014.02.003},
volume = {4},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Bentley,P
AU  - Ganesalingam,J
AU  - Jones,ALC
AU  - Mahady,K
AU  - Epton,S
AU  - Rinne,P
AU  - Sharma,P
AU  - Halse,O
AU  - Mehta,A
AU  - Rueckert,D
DO  - 10.1016/j.nicl.2014.02.003
EP  - 640
PY  - 2014///
SN  - 2213-1582
SP  - 635
TI  - Prediction of stroke thrombolysis outcome using CT brain machine learning
T2  - NeuroImage: Clinical
UR  - http://dx.doi.org/10.1016/j.nicl.2014.02.003
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000349667600069&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://www.sciencedirect.com/science/article/pii/S2213158214000217?via%3Dihub
UR  - http://hdl.handle.net/10044/1/84299
VL  - 4
ER  -
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