Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features
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Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features. / Grosser, Malte; Gellißen, Susanne; Borchert, Patrick; Sedlacik, Jan; Nawabi, Jawed; Fiehler, Jens; Forkert, Nils Daniel.
in: PLOS ONE, Jahrgang 15, Nr. 1, 2020, S. e0228113.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - Improved multi-parametric prediction of tissue outcome in acute ischemic stroke patients using spatial features
AU - Grosser, Malte
AU - Gellißen, Susanne
AU - Borchert, Patrick
AU - Sedlacik, Jan
AU - Nawabi, Jawed
AU - Fiehler, Jens
AU - Forkert, Nils Daniel
PY - 2020
Y1 - 2020
N2 - INTRODUCTION: 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.RESULTS: 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.CONCLUSION: Spatial features should be integrated to improve lesion outcome prediction using machine learning models.
AB - INTRODUCTION: 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.RESULTS: 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.CONCLUSION: Spatial features should be integrated to improve lesion outcome prediction using machine learning models.
KW - Acute Disease
KW - Aged
KW - Algorithms
KW - Area Under Curve
KW - Brain Infarction/diagnosis
KW - Female
KW - Humans
KW - Magnetic Resonance Imaging
KW - Male
KW - Middle Aged
KW - Models, Theoretical
KW - ROC Curve
KW - Stroke/diagnosis
U2 - 10.1371/journal.pone.0228113
DO - 10.1371/journal.pone.0228113
M3 - SCORING: Journal article
C2 - 31978179
VL - 15
SP - e0228113
JO - PLOS ONE
JF - PLOS ONE
SN - 1932-6203
IS - 1
ER -