Validity of Shape as a Predictive Biomarker of Final Infarct Volume in Acute Ischemic Stroke

Frindel, Carole; Rouanet, Anaïs; Giacalone, Mathilde; Cho, Tae-Hee; Østergaard, Leif; Fiehler, Jens; Pedraza, Salvador; Wiart, Marlène; Berthezène, Yves; Nighoghossian, Norbert; Rousseau, D.

doi:10.1161/strokeaha.114.008046

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…In order to simulate shape variability, we need to use shape models representative of the shape variability observed on clinical data. We studied the european I‐KNOW database and constructed, from real patient images, four shape models (

R_{1}

R_{2}

R_{3}

, and

R_{4}

) with ischemic lesion shapes and tissue distributions representative of the variability observed in the database (see Fig. ).…”

Section: Methodsmentioning

confidence: 99%

Robustness of spatio‐temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

Giacalone

Frindel

Robini

et al. 2016

Magnetic Resonance in Med

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R_{1}

R_{2}

R_{3}

, and

R_{4}

) with ischemic lesion shapes and tissue distributions representative of the variability observed in the database (see Fig. ).…”

Section: Methodsmentioning

confidence: 99%

Robustness of spatio‐temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

Giacalone

Frindel

Robini

et al. 2016

Magnetic Resonance in Med

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“…A large inter-patient variability remains, and direct integration of clinical variables was not found relevant. A better use of clinical information, more advanced filtering methods (Forkert et al, 2013) and additional MRI contrast parameters (Mouridsen, Hansen, Østergaard, & Jespersen, 2014) or spatial descriptors (Frindel et al, 2015) may improve the accuracy of our predictive model. However, unexpected sources of inaccuracy due to spontaneous reperfusion or lesion atrophy may persist.…”

Section: Discussionmentioning

confidence: 99%

Individualized quantification of the benefit from reperfusion therapy using stroke predictive models

Ozenne

Cho

Mikkelsen³

et al. 2019

Eur J of Neuroscience

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Purpose Recent imaging developments have shown the potential of voxel‐based models in assessing infarct growth after stroke. Many models have been proposed but their relevance in predicting the benefit of a reperfusion therapy remains unclear. We searched for a predictive model whose volumetric predictions would identify stroke patients who are to benefit from tissue plasminogen activator (t‐PA)‐induced reperfusion. Material and Methods Forty‐five cases were used to study retrospectively stroke progression from admission to end of follow‐up. Predictive approaches based on various statistical models, predictive variables and spatial filtering methods were compared. The optimal approach was chosen according to the area under the precision‐recall curve (AUPRC). The final lesion volume was then predicted assuming that the patient would or would not reperfuse. Patients, with an acute lesion of ≤50 ml and a predicted reduction in the presence of reperfusion >6 ml and >25% of the acute lesion, were classified as responders. Results The optimal model was a logistic regression using the voxel distance to the acute lesion, the volume of the acute lesion and Gaussian‐filtered MRI contrast parameters as predictive variables. The predictions gave a median AUPRC of 0.655, a median AUC of 0.976 and a median volumetric error of 8.29 ml. Nineteen patients matched the responder profile. A non‐significant trend of improved reduction in NIHSS score (−42.8%, p = .09) and in lesion volume (−78.1%, p = 0.21) following reperfusion was observed for responder patients. Conclusion Despite limited volumetric accuracy, predictive stroke models can be used to quantify the benefit of reperfusion therapies.

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“…Automatic quantification of these features extracts information from imaging data that is not accessible by qualitative visual rating [16][17][18][19]. Analysis of three-dimensional stroke lesion morphology has been used to characterize disease progression in small vessel disease [17] or lesion development in acute ischemic stroke [12]. However, to our knowledge, we provide the first study of lesion morphology as potential indicator of stroke etiology.…”

Section: Cardioembolic Stroke (Ascod C1 C2)mentioning

confidence: 99%

“…Stroke lesions involving multiple vascular territories would, therefore, result in large bounding box volumes. In addition, multiple, small and scattered lesions would lead to a very high ratio between the volume of the minimum oriented bounding box and lesion volume, whereas this ratio would be smaller for a single lesion of identical volume as illustrated in Fig 2. The ratio between bounding box and lesion volume also quantifies the more or less holey structure of a lesion and has previously been shown to be of value in a model predicting final infarct volumes [12]. In our group of patients, 11 of 37 patients with a potential cardioembolic etiology displayed lesions located in at least two of the main arterial territories of the brain (left or right internal carotid artery or posterior circulation territory), whereas this pattern was only found in 6 of 54 non-cardioembolic stroke patients.…”

Section: Cardioembolic Stroke (Ascod C1 C2)mentioning

confidence: 99%

Stroke Subtype Classification by Geometrical Descriptors of Lesion Shape

Cheng¹

2017

Preprint

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Background and purpose Inference of etiology from lesion pattern in acute magnetic resonance imaging is valuable for management and prognosis of acute stroke patients. This study aims to assess the value of three-dimensional geometrical lesion-shape descriptors for stroke-subtype classification, specifically regarding stroke of cardioembolic origin. Methods Stroke Etiology was classified according to ASCOD in retrospectively selected patients with acute stroke. Lesions were segmented on diffusion-weighed datasets, and descriptors of lesion shape quantified: surface area, sphericity, bounding box volume, and ratio between bounding box and lesion volume. Morphological measures were compared between stroke subtypes classified by ASCOD and between patients with embolic stroke of cardiac and non-cardiac source. Results 150 patients (mean age 77 years; 95% CI, 65-80 years; median NIHSS 6, range 0-22) were included. Group comparison of lesion shape measures demonstrated that lesions caused by small-vessel disease were smaller and more spherical compared to other stroke subtypes. No significant differences of morphological measures were detected between patients with cardioembolic and non-cardioembolic stroke. Conclusion Stroke lesions caused by small vessel disease can be distinguished from other stroke lesions based on distinctive morphological properties. However, within the group of

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Validity of Shape as a Predictive Biomarker of Final Infarct Volume in Acute Ischemic Stroke

Abstract: 2=0.98 in model with volume and shape). Conclusions-Our findings suggest that lesion shape contains important predictive information and reflects important environmental factors that might determine the progression of ischemia from the core. (Stroke. 2015;46:976-981.

Cited by 16 publications

References 29 publications

Robustness of spatio‐temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

Robustness of spatio‐temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke

Individualized quantification of the benefit from reperfusion therapy using stroke predictive models

Stroke Subtype Classification by Geometrical Descriptors of Lesion Shape

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