Abstract
The overarching aim of this thesis was to assess and identify imaging features that affect patient prognosis and efficacy of EVT for ischemic stroke, with the ultimate goal of imaging-based prediction of functional outcome and benefit of EVT in individual patients. The specific aims are:
• To evaluate methods for (automated) assessment of prognostic imaging features in ischemic stroke.
• To assess the influence of pre-existent imaging features of brain frailty on functional outcome and efficacy of EVT.
• To determine the value of CT imaging performed at admission for prediction of
functional outcome and benefit of EVT in individual patients.
The use of imaging in ischemic stroke is becoming increasingly versatile. AI-driven
software tools hold considerable potential to help radiologists accelerate assessment of stroke imaging and expedite treatment decision-making. To increase the clinical utility of these tools, further improvement of their performance is needed, specifically where human performance is suboptimal. Besides its role for diagnosing ischemic stroke, CT imaging also holds important information for predicting functional outcome and benefit of EVT in individual patients. However, in order to make the most accurate predictions, clinical and imaging variables should be combined and not used in isolation. Finally, identification of imaging markers to evaluate prognosis early after EVT will help to pave the way for advancing treatment of ischemic stroke and further improving patient outcomes.
• To evaluate methods for (automated) assessment of prognostic imaging features in ischemic stroke.
• To assess the influence of pre-existent imaging features of brain frailty on functional outcome and efficacy of EVT.
• To determine the value of CT imaging performed at admission for prediction of
functional outcome and benefit of EVT in individual patients.
The use of imaging in ischemic stroke is becoming increasingly versatile. AI-driven
software tools hold considerable potential to help radiologists accelerate assessment of stroke imaging and expedite treatment decision-making. To increase the clinical utility of these tools, further improvement of their performance is needed, specifically where human performance is suboptimal. Besides its role for diagnosing ischemic stroke, CT imaging also holds important information for predicting functional outcome and benefit of EVT in individual patients. However, in order to make the most accurate predictions, clinical and imaging variables should be combined and not used in isolation. Finally, identification of imaging markers to evaluate prognosis early after EVT will help to pave the way for advancing treatment of ischemic stroke and further improving patient outcomes.
Original language | English |
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Awarding Institution |
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Supervisors/Advisors |
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Award date | 1 May 2024 |
Place of Publication | Rotterdam |
Print ISBNs | 978-94-6483-925-8 |
Publication status | Published - 1 May 2024 |