Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features

Autoři: Malte Grosser aff001;  Susanne Gellißen aff001;  Patrick Borchert aff001;  Jan Sedlacik aff001;  Jawed Nawabi aff001;  Jens Fiehler aff001;  Nils Daniel Forkert aff002
Působiště autorů: Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-Eppendorf, Germany aff001;  Department of Radiology and Hotchkiss Brain Institute, University of Calgary, Calgary, Canada aff002
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0228113



In recent years, numerous methods have been proposed to predict tissue outcome in acute stroke patients using machine learning methods incorporating multiparametric imaging data. Most methods include diffusion and perfusion parameters as image-based parameters but do not include any spatial information although these parameters are spatially dependent, e.g. different perfusion properties in white and gray brain matter. This study aims to investigate if including spatial features improves the accuracy of multi-parametric tissue outcome prediction.

Materials and methods

Acute and follow-up multi-center MRI datasets of 99 patients were available for this study. Logistic regression, random forest, and XGBoost machine learning models were trained and tested using acute MR diffusion and perfusion features and known follow-up lesions. Different combinations of atlas coordinates and lesion probability maps were included as spatial information. The stroke lesion predictions were compared to the true tissue outcomes using the area under the receiver operating characteristic curve (ROC AUC) and the Dice metric.


The statistical analysis revealed that including spatial features significantly improves the tissue outcome prediction. Overall, the XGBoost and random forest models performed best in every setting and achieved state-of-the-art results regarding both metrics with similar improvements achieved including Montreal Neurological Institute (MNI) reference space coordinates or voxel-wise lesion probabilities.


Spatial features should be integrated to improve lesion outcome prediction using machine learning models.

Klíčová slova:

Diffusion weighted imaging – Forecasting – Ischemic stroke – Lesions – Magnetic resonance imaging – Machine learning – Permutation – Stroke


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Článek vyšel v časopise


2020 Číslo 1