Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

Anthony J. Winder; Susanne Siemonsen; Fabian Flottmann; Jens Fiehler; Nils D. Forkert

doi:10.1117/12.2254118

3 March 2017 Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention

Anthony J. Winder, Susanne Siemonsen, Fabian Flottmann, Jens Fiehler, Nils D. Forkert

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101344B (2017) https://doi.org/10.1117/12.2254118
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Voxel-based tissue outcome prediction in acute ischemic stroke patients is highly relevant for both clinical routine and research. Previous research has shown that features extracted from baseline multi-parametric MRI datasets have a high predictive value and can be used for the training of classifiers, which can generate tissue outcome predictions for both intravenous and conservative treatments. However, with the recent advent and popularization of intra-arterial thrombectomy treatment, novel research specifically addressing the utility of predictive classi- fiers for thrombectomy intervention is necessary for a holistic understanding of current stroke treatment options. The aim of this work was to develop three clinically viable tissue outcome prediction models using approximate nearest-neighbor, generalized linear model, and random decision forest approaches and to evaluate the accuracy of predicting tissue outcome after intra-arterial treatment. Therefore, the three machine learning models were trained, evaluated, and compared using datasets of 42 acute ischemic stroke patients treated with intra-arterial thrombectomy. Classifier training utilized eight voxel-based features extracted from baseline MRI datasets and five global features. Evaluation of classifier-based predictions was performed via comparison to the known tissue outcome, which was determined in follow-up imaging, using the Dice coefficient and leave-on-patient-out cross validation. The random decision forest prediction model led to the best tissue outcome predictions with a mean Dice coefficient of 0.37. The approximate nearest-neighbor and generalized linear model performed equally suboptimally with average Dice coefficients of 0.28 and 0.27 respectively, suggesting that both non-linearity and machine learning are desirable properties of a classifier well-suited to the intra-arterial tissue outcome prediction problem.

Citation Download Citation

Anthony J. Winder, Susanne Siemonsen, Fabian Flottmann, Jens Fiehler, and Nils D. Forkert "Comparison of classification methods for voxel-based prediction of acute ischemic stroke outcome following intra-arterial intervention", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101344B (3 March 2017); https://doi.org/10.1117/12.2254118

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