4.6 Article

Tissue Outcome Prediction in Patients with Proximal Vessel Occlusion and Mechanical Thrombectomy Using Logistic Models

Related references

Note: Only part of the references are listed.
Article Clinical Neurology

Post-stroke outcomes predicted from multivariate lesion-behaviour and lesion network mapping

Mark Bowren et al.

Summary: Predicting chronic post-stroke outcomes is challenging due to individual variability and reliance on clinical heuristics. This study explores the use of lesion-behaviour mapping and structural and functional lesion networks to improve prediction of 12-month cognitive and motor outcomes in stroke patients. The results show that both methods can significantly predict variance in outcomes, with variations in performance for specific deficits.

BRAIN (2022)

Article Radiology, Nuclear Medicine & Medical Imaging

Probability maps classify ischemic stroke regions more accurately than CT perfusion summary maps

Daan Peerlings et al.

Summary: This study compares single parameter thresholding with multivariable probabilistic classification in the analysis of computed tomography perfusion (CTP) parameter maps for identifying ischemic stroke regions. The results show that multivariable probability maps outperform single parameter thresholding in estimating the infarct lesion.

EUROPEAN RADIOLOGY (2022)

Article Endocrinology & Metabolism

Integrating regional perfusion CT information to improve prediction of infarction after stroke

Julian Klug et al.

Summary: The study showed that integrating perfusion information from adjacent tissues improves the prediction of infarction, with the best performance observed in models using Tmax and multiple perfusion parameters. This approach may help better identify ischemic core and penumbra thresholds, as well as improve patient selection for acute stroke treatment in future studies.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2021)

Article Clinical Neurology

Computed Tomography Perfusion-Based Machine Learning Model Better Predicts Follow-Up Infarction in Patients With Acute Ischemic Stroke

Hulin Kuang et al.

Summary: In this study, a threshold-free computed tomography perfusion-based machine learning model was developed to predict follow-up infarction in patients with acute ischemic stroke. The ML model demonstrated a high correlation and small volumetric difference with actual follow-up infarct volume, outperforming current methods. The use of computed tomography perfusion data and time estimates in the ML model resulted in more accurate predictions.

STROKE (2021)

Article Clinical Neurology

Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning Lessons From the ISLES Challenge

Arsany Hakim et al.

Summary: The ISLES challenge aims to develop advanced tools for stroke lesion analysis using machine learning. Based on diffusion-weighted imaging, the aim of ISLES-2018 was to segment infarcted tissue on CTP. Twenty-four teams participated in the challenge, with the top team's algorithm outperforming conventional methods, showing improved accuracy in predicting infarcted tissue from CTP.

STROKE (2021)

Article Clinical Neurology

Contrast Bolus Interference in a Multimodal CT Stroke Protocol

E. Kellner et al.

Summary: The study compared the effects of performing CTP before and after CTA in a large cohort of stroke patients, finding that CTP can be reliably performed after CTA without significant interference. However, caution should be taken to avoid errors caused by CTA bolus interference with CTP measurements.

AMERICAN JOURNAL OF NEURORADIOLOGY (2021)

Article Neuroimaging

Impact of the reperfusion status for predicting the final stroke infarct using deep learning

Noelie Debs et al.

Summary: The study assessed the impact of integrating reperfusion status into deep learning models for predicting the final infarct in acute ischemic stroke patients, finding that CNN-based models outperformed clinically-used perfusion-diffusion mismatch models in terms of performance. Comparing predicted infarct in cases of successful vs failed reperfusion may aid in estimating treatment effect and guiding therapeutic decisions for selected patients.

NEUROIMAGE-CLINICAL (2021)

Article Medicine, General & Internal

Thrombectomy for Stroke at 6 to 16 Hours with Selection by Perfusion Imaging

G. W. Albers et al.

NEW ENGLAND JOURNAL OF MEDICINE (2018)

Article Medicine, General & Internal

Thrombectomy 6 to 24 Hours after Stroke with a Mismatch between Deficit and Infarct

R. G. Nogueira et al.

NEW ENGLAND JOURNAL OF MEDICINE (2018)

Article Endocrinology & Metabolism

Fully automated stroke tissue estimation using random forest classifiers (FASTER)

Richard McKinley et al.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2017)

Article Endocrinology & Metabolism

A benchmarking tool to evaluate computer tomography perfusion infarct core predictions against a DWI standard

Carlo W. Cereda et al.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2016)

Article Biochemical Research Methods

The first step for neuroimaging data analysis: DICOM to NIfTI conversion

Xiangrui Li et al.

JOURNAL OF NEUROSCIENCE METHODS (2016)

Article Endocrinology & Metabolism

Multivariate dynamic prediction of ischemic infarction and tissue salvage as a function of time and degree of recanalization

Andre Kemmling et al.

JOURNAL OF CEREBRAL BLOOD FLOW AND METABOLISM (2015)

Article Neuroimaging

Fast semi-automated lesion demarcation in stroke

Bianca de Haan et al.

NEUROIMAGE-CLINICAL (2015)

Article Neuroimaging

Predicting outcome and recovery after stroke with lesions extracted from MRI images

Thomas M. H. Hope et al.

NEUROIMAGE-CLINICAL (2013)

Article Neurosciences

Age-specific CT and MRI templates for spatial normalization

Christopher Rorden et al.

NEUROIMAGE (2012)

Article Clinical Neurology

Infarct Volume Is a Pivotal Biomarker After Intra-Arterial Stroke Therapy

Albert J. Yoo et al.

STROKE (2012)

Article Clinical Neurology

Cerebral Blood Flow Is the Optimal CT Perfusion Parameter for Assessing Infarct Core

Bruce C. V. Campbell et al.

STROKE (2011)

Review Engineering, Biomedical

Deconvolution-Based CT and MR Brain Perfusion Measurement: Theoretical Model Revisited and Practical Implementation Details

Andreas Fieselmann et al.

INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING (2011)

Article Clinical Neurology

Optimal Tmax Threshold for Predicting Penumbral Tissue in Acute Stroke

Jean-Marc Olivot et al.

STROKE (2009)