Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information.

Pinto, Adriano; McKinley, Richard; Alves, Victor; Wiest, Roland; Silva, Carlos A; Reyes, Mauricio (2018). Stroke Lesion Outcome Prediction Based on MRI Imaging Combined With Clinical Information. Frontiers in neurology, 9(1060), p. 1060. Frontiers Media S.A. 10.3389/fneur.2018.01060

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In developed countries, the second leading cause of death is stroke, which has the ischemic stroke as the most common type. The preferred diagnosis procedure involves the acquisition of multi-modal Magnetic Resonance Imaging. Besides detecting and locating the stroke lesion, Magnetic Resonance Imaging captures blood flow dynamics that guides the physician in evaluating the risks and benefits of the reperfusion procedure. However, the decision process is an intricate task due to the variability of lesion size, shape, and location, as well as the complexity of the underlying cerebral hemodynamic process. Therefore, an automatic method that predicts the stroke lesion outcome, at a 3-month follow-up, would provide an important support to the physicians' decision process. In this work, we propose an automatic deep learning-based method for stroke lesion outcome prediction. Our main contribution resides in the combination of multi-modal Magnetic Resonance Imaging maps with non-imaging clinical meta-data: the thrombolysis in cerebral infarction scale, which categorizes the success of recanalization, achieved through mechanical thrombectomy. In our proposal, this clinical information is considered at two levels. First, at a population level by embedding the clinical information in a custom loss function used during training of our deep learning architecture. Second, at a patient-level through an extra input channel of the neural network used at testing time for a given patient case. By merging imaging with non-imaging clinical information, we aim to obtain a model aware of the principal and collateral blood flow dynamics for cases where there is no perfusion beyond the point of occlusion and for cases where the perfusion is complete after the occlusion point.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic and Interventional Neuroradiology
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

McKinley, Richard; Wiest, Roland and Reyes, Mauricio

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology

ISSN:

1664-2295

Publisher:

Frontiers Media S.A.

Language:

English

Submitter:

Martin Zbinden

Date Deposited:

04 Feb 2019 10:40

Last Modified:

10 Feb 2019 02:38

Publisher DOI:

10.3389/fneur.2018.01060

PubMed ID:

30568631

Uncontrolled Keywords:

MRI deep learning machine learning prediction stroke

BORIS DOI:

10.7892/boris.123001

URI:

https://boris.unibe.ch/id/eprint/123001

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