Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients
Introduction In stroke patients, accurate infarct volume assessment at 24 hours typically requires manual segmentation of lesions that often are not well defined. We aimed to validate an automated machine learning algorithm (MethinksFIV) specifically trained to automatically segment and measure suba...
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Wiley
2023-11-01
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| Series: | Stroke: Vascular and Interventional Neurology |
| Online Access: | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.268 |
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| author | Magda Jablonska João André Sousa Marc Molina Pere Canals Alvaro Garcia‐Tornel Marc Rodrigo‐Gisbert Federica Rizzo Marta Olivé‐Gadea Manuel Requena David Rodriguez‐Luna Noelia Rodriguez‐Villatoro Jesús M. Juega Marián Muchada Jorge Pagola Marta Rubiera Alejandro Tomasello Carlos A. Molina Leonardo Tanzi Victor Salvia Cristian Marti Marc Ribo |
| author_facet | Magda Jablonska João André Sousa Marc Molina Pere Canals Alvaro Garcia‐Tornel Marc Rodrigo‐Gisbert Federica Rizzo Marta Olivé‐Gadea Manuel Requena David Rodriguez‐Luna Noelia Rodriguez‐Villatoro Jesús M. Juega Marián Muchada Jorge Pagola Marta Rubiera Alejandro Tomasello Carlos A. Molina Leonardo Tanzi Victor Salvia Cristian Marti Marc Ribo |
| author_sort | Magda Jablonska |
| collection | DOAJ |
| description | Introduction In stroke patients, accurate infarct volume assessment at 24 hours typically requires manual segmentation of lesions that often are not well defined. We aimed to validate an automated machine learning algorithm (MethinksFIV) specifically trained to automatically segment and measure subacute infarcts on non‐contrast CT (NCCT) scans. Methods We retrospectively studied stroke patients with a large vessel occlusion prospectively admitted to a comprehensive stroke center. The final infarct volume was segmented on the 24h NCCT manually (man‐FIV) and with the automated method (auto‐FIV). Both measurements were correlated with clinical outcomes. Results We included 346 patients. On 24h‐NCCT median auto‐FIV was lower than man‐FIV (8.6 ml [2.3‐40.7] vs. 16.9 ml [1.1‐70.8], p<0.001). Auto‐FIV and man‐FIV were highly correlated (r=0.8; p<0.001; mean dice coefficient of 0.61). The Auto‐FIV correlated better than man‐FIV with 24h (r=0.54 vs. r=0.51, both p<0.001) and discharge NIHSS (r=0.59 vs r=0.54, both p<0.001). An increase of 1 ml in auto‐FIV was associated with an increase in the odds of higher ordinal mRS at three months (OR=1.01 95%CI 1.01‐1.02, p<0.001). Auto‐FIV was a better predictor of mRS>2 at three months (OR: 1.02 95%CI 1.01‐1.13, p=0.002) than manual segmentation (OR=1.01, 95%CI 1.00‐1.01, p=0.01). In a multivariable analysis, only auto‐FIV remained a significant predictor of mRS>2 at three months (aOR: 1.03 95% CI 1.01‐1.05, p=0.004). Conclusion An automated method to measure subacute infarct volumes in stroke patients is accurate and correlates well with further clinical outcomes. The method appears to outperform manual segmentation and may be used to facilitate infarct characterization in large clinical trials. |
| format | Article |
| id | doaj-art-e29d6ea02a7e490fafc6d62f95e122cb |
| institution | DOAJ |
| issn | 2694-5746 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Wiley |
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| series | Stroke: Vascular and Interventional Neurology |
| spelling | doaj-art-e29d6ea02a7e490fafc6d62f95e122cb2025-08-20T03:13:36ZengWileyStroke: Vascular and Interventional Neurology2694-57462023-11-013S210.1161/SVIN.03.suppl_2.268Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patientsMagda Jablonska0João André Sousa1Marc Molina2Pere Canals3Alvaro Garcia‐Tornel4Marc Rodrigo‐Gisbert5Federica Rizzo6Marta Olivé‐Gadea7Manuel Requena8David Rodriguez‐Luna9Noelia Rodriguez‐Villatoro10Jesús M. Juega11Marián Muchada12Jorge Pagola13Marta Rubiera14Alejandro Tomasello15Carlos A. Molina16Leonardo Tanzi17Victor Salvia18Cristian Marti19Marc Ribo20Stroke Unit Vall d’Hebron University Hospital Barcelona SpainNeurology Department, Centro Hospitalar e Universitario de Coimbra Coimbra PortugalStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainNeurointerventional Radiology Department Vall d’Hebron University Hospital Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainMethinks Software Barcelona SpainMethinks Software Barcelona SpainMethinks Software Barcelona SpainStroke Unit Vall d’Hebron University Hospital Barcelona SpainIntroduction In stroke patients, accurate infarct volume assessment at 24 hours typically requires manual segmentation of lesions that often are not well defined. We aimed to validate an automated machine learning algorithm (MethinksFIV) specifically trained to automatically segment and measure subacute infarcts on non‐contrast CT (NCCT) scans. Methods We retrospectively studied stroke patients with a large vessel occlusion prospectively admitted to a comprehensive stroke center. The final infarct volume was segmented on the 24h NCCT manually (man‐FIV) and with the automated method (auto‐FIV). Both measurements were correlated with clinical outcomes. Results We included 346 patients. On 24h‐NCCT median auto‐FIV was lower than man‐FIV (8.6 ml [2.3‐40.7] vs. 16.9 ml [1.1‐70.8], p<0.001). Auto‐FIV and man‐FIV were highly correlated (r=0.8; p<0.001; mean dice coefficient of 0.61). The Auto‐FIV correlated better than man‐FIV with 24h (r=0.54 vs. r=0.51, both p<0.001) and discharge NIHSS (r=0.59 vs r=0.54, both p<0.001). An increase of 1 ml in auto‐FIV was associated with an increase in the odds of higher ordinal mRS at three months (OR=1.01 95%CI 1.01‐1.02, p<0.001). Auto‐FIV was a better predictor of mRS>2 at three months (OR: 1.02 95%CI 1.01‐1.13, p=0.002) than manual segmentation (OR=1.01, 95%CI 1.00‐1.01, p=0.01). In a multivariable analysis, only auto‐FIV remained a significant predictor of mRS>2 at three months (aOR: 1.03 95% CI 1.01‐1.05, p=0.004). Conclusion An automated method to measure subacute infarct volumes in stroke patients is accurate and correlates well with further clinical outcomes. The method appears to outperform manual segmentation and may be used to facilitate infarct characterization in large clinical trials.https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.268 |
| spellingShingle | Magda Jablonska João André Sousa Marc Molina Pere Canals Alvaro Garcia‐Tornel Marc Rodrigo‐Gisbert Federica Rizzo Marta Olivé‐Gadea Manuel Requena David Rodriguez‐Luna Noelia Rodriguez‐Villatoro Jesús M. Juega Marián Muchada Jorge Pagola Marta Rubiera Alejandro Tomasello Carlos A. Molina Leonardo Tanzi Victor Salvia Cristian Marti Marc Ribo Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients Stroke: Vascular and Interventional Neurology |
| title | Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients |
| title_full | Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients |
| title_fullStr | Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients |
| title_full_unstemmed | Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients |
| title_short | Abstract 268: Validation of an automated method to assess 24‐hour infarct volume in stroke patients |
| title_sort | abstract 268 validation of an automated method to assess 24 hour infarct volume in stroke patients |
| url | https://www.ahajournals.org/doi/10.1161/SVIN.03.suppl_2.268 |
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