In:
Frontiers in Neurology, Frontiers Media SA, Vol. 12 ( 2021-5-6)
Abstract:
Background: Clinical stroke rehabilitation decision making relies on multi-modal data, including imaging and other clinical assessments. However, most previously described methods for predicting long-term stroke outcomes do not make use of the full multi-modal data available. The aim of this work was to develop and evaluate the benefit of nested regression models that utilise clinical assessments as well as image-based biomarkers to model 30-day NIHSS. Method: 221 subjects were pooled from two prospective trials with follow-up MRI or CT scans, and NIHSS assessed at baseline, as well as 48-hours and 30 days after symptom onset. Three prediction models for 30-day NIHSS were developed using a support vector regression model: one clinical model based on modifiable and non-modifiable risk factors (M CLINICAL ) and two nested regression models that aggregate clinical and image-based features that differed with respect to the method used for selection of important brain regions for the modelling task. The first model used the widely accepted RreliefF (M RELIEF ) machine learning method for this purpose, while the second model employed a lesion-symptom mapping technique (M LSM ) often used in neuroscience to investigate structure-function relationships and identify eloquent regions in the brain. Results: The two nested models achieved a similar performance while considerably outperforming the clinical model. However, M RELIEF required fewer brain regions and achieved a lower mean absolute error than M LSM while being less computationally expensive. Conclusion: Aggregating clinical and imaging information leads to considerably better outcome prediction models. While lesion-symptom mapping is a useful tool to investigate structure-function relationships of the brain, it does not lead to better outcome predictions compared to a simple data-driven feature selection approach, which is less computationally expensive and easier to implement.
Type of Medium:
Online Resource
ISSN:
1664-2295
DOI:
10.3389/fneur.2021.663899
DOI:
10.3389/fneur.2021.663899.s001
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2021
detail.hit.zdb_id:
2564214-5
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