Abstract
Ischemic stroke is a debilitating brain injury affecting millions of people, causing long-term disabilities. Immediately after stroke, it is not easy to pre-dict the extent of the injury and its long-term effects, yet outcome prediction is desired to inform clinical decision-making processes. Apparent diffusion coefficient (ADC) maps, calculated from diffusion-weighted imaging, are widely used in clinics to diagnose and monitor ischemic stroke. Radiomics analysis is an emerging feature extraction method providing many quantitative imaging indicators from the ADC maps. Here, we have utilized these features to predict the clinical outcome of 43 ischemic stroke patients. We divided the clinical outcome into two groups (good and poor outcomes) based on the patients’ modified Rankin Scale scores and trained a binary classifier to predict the correct outcome group. We compared various machine learning classifiers and feature selection and pre-processing techniques to create a parsimonious mRS score prediction pipeline. Our results showed that the best-performing classifier was a multi-layer perceptron classifier which used three radiomics features to achieve a classification accuracy of 0.94. This is a marked improvement compared to our previous results, where the classification accuracy was around 0.7 and matches the performance of previous studies reported in the literature. In the clinics, our pipeline can help doctors and stroke patients plan recovery and rehabilitation processes.
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Acknowledgments
This work was funded by Aberystwyth University Centre for International Development Research at Aberystwyth (CIDRA) (grant number H1053–28, “Automatic Assessment of Gait Impairments in Stroke using Artificial Intelligence, Wearable Technology and Neuroimaging”); Welsh Government (Ser Cymru Cofund Fellowship, grant No. 663830-AU167, to OA, and Ser Cymru Tackling Covid-19, grant No. 009 to FVP and OA); Health and Care Research Wales (PhD Studentship to FVP and OA); Public Health Wales - Stroke Implementation Group (Stroke Research, Innovation, and Education fund to FVP and OA); European Commission (H2020-MSCA-RISE-2019, grant No. 873178, to OA); Turkey Council of Higher Education (YÖK) and Scientific and Technological Research Council of Turkey (TÜBİTAK) (PhD scholarship to MSE and ES).
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Erdoğan, M.Ş., Sümer, E., Villagra, F., Işık, E.Ö., Akanyeti, O., Saybaşılı, H. (2024). Clinical Outcome Prediction Pipeline for Ischemic Stroke Patients Using Radiomics Features and Machine Learning. In: Naik, N., Jenkins, P., Grace, P., Yang, L., Prajapat, S. (eds) Advances in Computational Intelligence Systems. UKCI 2023. Advances in Intelligent Systems and Computing, vol 1453. Springer, Cham. https://doi.org/10.1007/978-3-031-47508-5_39
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