Ischemic lesion volume prediction in thrombolysis treated wake-up stroke patients

Abstract There is growing research interest on identification of CT Perfusion (CTP) parameters that predict the outcome in acute ischemic stroke patients. The aim of this study is to produce the model, based on core-penumbra related parameters assessed by CTP processing and commonly used clinical prediction factors, to predict the final infarct volume in thrombolysis-treated anterior circulation wake-up stroke (WUS) patients. The study was conducted on 51 consecutive wake-up ischemic stroke patients. The model for the predictive estimation of final ischemic volume was determined by using the Least Absolute Shrinkage and Selection Operator (LASSO) regularized least-squares regression. The results showed that CTP core volume and CTP total ischemic volume at admission, together with ASPECT score predict the final infarct lesion volume. In particular, the identified model presented 5-fold cross-validation root mean square error RMSE of 8.1 ml and the coefficient of determination (R2=0.94) on our dataset. The results should be confirmed in a lager study. In conclusion, in this study we preliminarily identified a predictive model to estimate final ischemic lesion volume in thrombolysis treated wake-up stroke patients.

[1]  Ting-Yim Lee,et al.  Cerebral Blood Volume ASPECTS Is the Best Predictor of Clinical Outcome in Acute Ischemic Stroke: A Retrospective, Combined Semi-Quantitative and Quantitative Assessment , 2016, PloS one.

[2]  R. Hornung,et al.  Population-based study of wake-up strokes , 2011, Neurology.

[3]  Mark W Parsons,et al.  Perfusion CT: Is it Clinically Useful? , 2008, International journal of stroke : official journal of the International Stroke Society.

[4]  B. Campbell,et al.  Imaging-based selection for revascularization in acute ischemic stroke. , 2016, Current opinion in neurology.

[5]  Milos Ajcevic,et al.  Hyper-acute EEG alterations predict functional and morphological outcomes in thrombolysis-treated ischemic stroke: a wireless EEG study , 2020, Medical & Biological Engineering & Computing.

[6]  M. Chopp,et al.  Multiparametric MRI Tissue Characterization in Clinical Stroke With Correlation to Clinical Outcome: Part 2 , 2001, Stroke.

[7]  Reto Meuli,et al.  Perfusion-CT Assessment of Infarct Core and Penumbra: Receiver Operating Characteristic Curve Analysis in 130 Patients Suspected of Acute Hemispheric Stroke , 2006, Stroke.

[8]  W R Clarke,et al.  Baseline NIH Stroke Scale score strongly predicts outcome after stroke , 1999, Neurology.

[9]  Hamid Soltanian-Zadeh,et al.  Predicting final extent of ischemic infarction using an artificial neural network analysis of multiparametric MRI in patients with stroke , 2009, IJCNN.

[10]  Steve Doucette,et al.  CT Perfusion in Acute Stroke Predicts Final Infarct Volume- Inter-observer Study , 2016, Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques.

[11]  Hamid Soltanian-Zadeh,et al.  Multiparametric MRI ISODATA Ischemic Lesion Analysis: Correlation With the Clinical Neurological Deficit and Single-Parameter MRI Techniques , 2002, Stroke.

[12]  P. Widimsky,et al.  Acute stroke therapy: A review. , 2017, Trends in cardiovascular medicine.

[13]  Agostino Accardo,et al.  A CT perfusion based model predicts outcome in wake-up stroke patients treated with recombinant tissue plasminogen activator , 2020, Physiological Measurement.

[14]  P. Manganotti,et al.  Early EEG Alterations Correlate with CTP Hypoperfused Volumes and Neurological Deficit: A Wireless EEG Study in Hyper-Acute Ischemic Stroke , 2021, Annals of Biomedical Engineering.

[15]  J. Babb,et al.  Perfusion CT Improves Diagnostic Accuracy for Hyperacute Ischemic Stroke in the 3-Hour Window: Study of 100 Patients with Diffusion MRI Confirmation , 2009, Cerebrovascular Diseases.

[16]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[17]  Paolo Manganotti,et al.  Ischemic Volume and Neurological Deficit: Correlation of Computed Tomography Perfusion with the National Institutes of Health Stroke Scale Score in Acute Ischemic Stroke. , 2018, Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association.

[18]  Geoffrey A. Donnan,et al.  Extending thrombolysis to 4·5–9 h and wake-up stroke using perfusion imaging: a systematic review and meta-analysis of individual patient data , 2019, The Lancet.

[19]  Joseph P. Broderick,et al.  Tissue plasminogen activator for acute ischemic stroke. The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. , 1995 .

[20]  P. S. Jones,et al.  Does Perfusion Computed Tomography Facilitate Clinical Decision Making for Thrombolysis in Unselected Acute Patients with Suspected Ischaemic Stroke? , 2011, Cerebrovascular Diseases.

[21]  U. Schulz,et al.  Improving the Assessment of Outcomes in Stroke: Use of a Structured Interview to Assign Grades on the Modified Rankin Scale , 2002, Stroke.

[22]  Paolo Manganotti,et al.  Wake-up stroke: thrombolysis reduces ischemic lesion volume and neurological deficit , 2019, Journal of Neurology.

[23]  Bruce C V Campbell,et al.  CT perfusion improves diagnostic accuracy and confidence in acute ischaemic stroke , 2013, Journal of Neurology, Neurosurgery & Psychiatry.

[24]  Paolo Manganotti,et al.  A novel computed tomography perfusion-based quantitative tool for evaluation of perfusional abnormalities in migrainous aura stroke mimic , 2020, Neurological Sciences.

[25]  A. Demchuk,et al.  Validity and reliability of a quantitative computed tomography score in predicting outcome of hyperacute stroke before thrombolytic therapy , 2000, The Lancet.

[26]  Pedro Vilela,et al.  Brain ischemia: CT and MRI techniques in acute ischemic stroke. , 2017, European journal of radiology.

[27]  P. Gorelick,et al.  The global burden of stroke: persistent and disabling , 2019, The Lancet Neurology.

[28]  Hamid Soltanian-Zadeh,et al.  Predicting Final Extent of Ischemic Infarction Using Artificial Neural Network Analysis of Multi-Parametric MRI in Patients with Stroke , 2009, 2009 International Joint Conference on Neural Networks.