Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke
Background Vascular geometry plays an important role in stroke thrombectomy outcomes, but few studies have examined complex characteristics of vessel morphology. The authors hypothesized that engineered vessel cross‐section features could be used to predict thrombectomy first‐pass effect (FPE). Meth...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | Stroke: Vascular and Interventional Neurology |
| Subjects: | |
| Online Access: | https://www.ahajournals.org/doi/10.1161/SVIN.122.000646 |
| _version_ | 1827374781105700864 |
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| author | Seyyed Mostafa Mousavi Janbeh Sarayi Briana A. Santo Muhammad Waqas Andre Monterio Sricharan S. Veeturi TaJania D. Jenkins Elad I. Levy Adnan H. Siddiqui Vincent M. Tutino |
| author_facet | Seyyed Mostafa Mousavi Janbeh Sarayi Briana A. Santo Muhammad Waqas Andre Monterio Sricharan S. Veeturi TaJania D. Jenkins Elad I. Levy Adnan H. Siddiqui Vincent M. Tutino |
| author_sort | Seyyed Mostafa Mousavi Janbeh Sarayi |
| collection | DOAJ |
| description | Background Vascular geometry plays an important role in stroke thrombectomy outcomes, but few studies have examined complex characteristics of vessel morphology. The authors hypothesized that engineered vessel cross‐section features could be used to predict thrombectomy first‐pass effect (FPE). Methods The authors analyzed computed tomography angiography and noncontrast computed tomagraphy from 50 patients with anterior circulation stroke thrombectomy. After segmentation, traditional metrics (vessel tortuosity and angulation) were calculated from vessel centerlines that were transformed into the same coordinate system and same region of interest. Univariate statistical analysis and geometric morphometrics were used to interrogate differences in geometry between cases that did and did not achieve FPE, which classical angulation and tortuosity did not quantify. To describe these differences, complex cross‐section features were engineered and quantified using a semiautomatic pipeline. Machine learning was used to train predictive models of FPE based on significant cross‐section features. Results Only one local tortuosity metric was significantly different (q=0.019) between FPE and first‐pass failure cases. The most significant principal component score (q=0.012) from geometric morphometrics highlighted the M1 segment of the middle cerebral artery and upper cavernous internal carotid artery variation as morphological indicators of first‐pass outcome. Fifteen cross‐section features, corresponding to internal carotid artery and middle cerebral artery regions, were significantly different between cases that did and did not achieve FPE. Predictive models exhibited a strong prediction of FPE (area under the curve=0.98±0.05) and outperformed models using traditional tortuosity and angulation features. Conclusion Cross‐section features are a novel class of powerful and interpretable predictors of FPE, which could assist in treatment decision‐making. |
| first_indexed | 2024-03-08T11:39:58Z |
| format | Article |
| id | doaj.art-f9752cae298746a289a7643293bf267c |
| institution | Directory Open Access Journal |
| issn | 2694-5746 |
| language | English |
| last_indexed | 2024-03-08T11:39:58Z |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke: Vascular and Interventional Neurology |
| spelling | doaj.art-f9752cae298746a289a7643293bf267c2024-01-25T09:28:49ZengWileyStroke: Vascular and Interventional Neurology2694-57462023-03-013210.1161/SVIN.122.000646Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic StrokeSeyyed Mostafa Mousavi Janbeh Sarayi0Briana A. Santo1Muhammad Waqas2Andre Monterio3Sricharan S. Veeturi4TaJania D. Jenkins5Elad I. Levy6Adnan H. Siddiqui7Vincent M. Tutino8Canon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYCanon Stroke and Vascular Research Center University at Buffalo Buffalo NYBackground Vascular geometry plays an important role in stroke thrombectomy outcomes, but few studies have examined complex characteristics of vessel morphology. The authors hypothesized that engineered vessel cross‐section features could be used to predict thrombectomy first‐pass effect (FPE). Methods The authors analyzed computed tomography angiography and noncontrast computed tomagraphy from 50 patients with anterior circulation stroke thrombectomy. After segmentation, traditional metrics (vessel tortuosity and angulation) were calculated from vessel centerlines that were transformed into the same coordinate system and same region of interest. Univariate statistical analysis and geometric morphometrics were used to interrogate differences in geometry between cases that did and did not achieve FPE, which classical angulation and tortuosity did not quantify. To describe these differences, complex cross‐section features were engineered and quantified using a semiautomatic pipeline. Machine learning was used to train predictive models of FPE based on significant cross‐section features. Results Only one local tortuosity metric was significantly different (q=0.019) between FPE and first‐pass failure cases. The most significant principal component score (q=0.012) from geometric morphometrics highlighted the M1 segment of the middle cerebral artery and upper cavernous internal carotid artery variation as morphological indicators of first‐pass outcome. Fifteen cross‐section features, corresponding to internal carotid artery and middle cerebral artery regions, were significantly different between cases that did and did not achieve FPE. Predictive models exhibited a strong prediction of FPE (area under the curve=0.98±0.05) and outperformed models using traditional tortuosity and angulation features. Conclusion Cross‐section features are a novel class of powerful and interpretable predictors of FPE, which could assist in treatment decision‐making.https://www.ahajournals.org/doi/10.1161/SVIN.122.000646acute ischemic strokefirst‐pass effectgeometric morphometricsmechanical thrombectomyvessel morphology |
| spellingShingle | Seyyed Mostafa Mousavi Janbeh Sarayi Briana A. Santo Muhammad Waqas Andre Monterio Sricharan S. Veeturi TaJania D. Jenkins Elad I. Levy Adnan H. Siddiqui Vincent M. Tutino Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke Stroke: Vascular and Interventional Neurology acute ischemic stroke first‐pass effect geometric morphometrics mechanical thrombectomy vessel morphology |
| title | Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke |
| title_full | Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke |
| title_fullStr | Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke |
| title_full_unstemmed | Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke |
| title_short | Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke |
| title_sort | vascular cross section rather than tortuosity can classify first pass outcome of mechanical thrombectomy for ischemic stroke |
| topic | acute ischemic stroke first‐pass effect geometric morphometrics mechanical thrombectomy vessel morphology |
| url | https://www.ahajournals.org/doi/10.1161/SVIN.122.000646 |
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