Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy
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| Titel: | Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy |
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| Autoren: | Herzog, Lisa, Kook, Lucas, Hamann, Janne, Globas, Christoph, Heldner, Mirjam R, Seiffge, David, Antonenko, Kateryna, Dobrocky, Tomas, Panos, Leonidas, Kaesmacher, Johannes, Fischer, Urs, Gralla, Jan, Arnold, Marcel, Wiest, Roland, Luft, Andreas R, Sick, Beate, Wegener, Susanne |
| Weitere Verfasser: | University of Zurich, Wegener, Susanne |
| Quelle: | Stroke. 54:1761-1769 |
| Verlagsinformationen: | Ovid Technologies (Wolters Kluwer Health), 2023. |
| Publikationsjahr: | 2023 |
| Schlagwörter: | 2902 Advanced and Specialized Nursing, 610 Medicine & health, 10060 Epidemiology, Biostatistics and Prevention Institute (EBPI), Prognosis, stroke, 2705 Cardiology and Cardiovascular Medicine, outcome prediction, 10040 Clinic for Neurology, Brain Ischemia, Stroke, 2728 Neurology (clinical), machine learning, Deep Learning, Treatment Outcome, Humans, Neurologists, Thrombectomy, Ischemic Stroke, Retrospective Studies |
| Beschreibung: | BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models using clinical and magnetic resonance imaging data? METHODS: In this observational study, we collected data of 222 patients with middle cerebral artery M1 segment occlusion who received mechanical thrombectomy. In a 5-fold cross validation, we evaluated interpretable deep learning models for predicting functional outcome in terms of modified Rankin scale at 3 months using clinical variables, diffusion weighted imaging and perfusion weighted imaging, and a combination thereof. Based on 50 test patients, we compared model performances to those of 5 experienced stroke neurologists. Prediction performance for ordinal (modified Rankin scale score, 0–6) and binary (modified Rankin scale score, 0–2 versus 3–6) functional outcome was assessed using discrimination and calibration measures like area under the receiver operating characteristic curve and accuracy (percentage of correctly classified patients). RESULTS: In the cross validation, the model based on clinical variables and diffusion weighted imaging achieved the highest binary prediction performance (area under the receiver operating characteristic curve, 0.766 [0.727–0.803]). Performance of models using clinical variables or diffusion weighted imaging only was lower. Adding perfusion weighted imaging did not improve outcome prediction. On the test set of 50 patients, binary prediction performance between model (accuracy, 60% [55.4%–64.4%]) and neurologists (accuracy, 60% [55.8%–64.21%]) was similar when using clinical data. However, models significantly outperformed neurologists when imaging data were provided, alone or in combination with clinical variables (accuracy, 72% [67.8%–76%] versus 64% [59.8%–68.4%] with clinical and imaging data). Prediction performance of neurologists with comparable experience varied strongly. CONCLUSIONS: We hypothesize that early prediction of functional outcome in large vessel occlusion stroke patients may be significantly improved if neurologists are supported by interpretable deep learning models. |
| Publikationsart: | Article Other literature type |
| Dateibeschreibung: | ZORA_252380.pdf - application/pdf |
| Sprache: | English |
| ISSN: | 1524-4628 0039-2499 |
| DOI: | 10.1161/strokeaha.123.042496 |
| DOI: | 10.5167/uzh-252380 |
| Zugangs-URL: | https://pubmed.ncbi.nlm.nih.gov/37313740 |
| Dokumentencode: | edsair.doi.dedup.....1d1fcfbbe1023f1e65cd39b92e443c96 |
| Datenbank: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstract: | BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction challenging. Can we improve estimation of functional outcome with interpretable deep learning models using clinical and magnetic resonance imaging data? METHODS: In this observational study, we collected data of 222 patients with middle cerebral artery M1 segment occlusion who received mechanical thrombectomy. In a 5-fold cross validation, we evaluated interpretable deep learning models for predicting functional outcome in terms of modified Rankin scale at 3 months using clinical variables, diffusion weighted imaging and perfusion weighted imaging, and a combination thereof. Based on 50 test patients, we compared model performances to those of 5 experienced stroke neurologists. Prediction performance for ordinal (modified Rankin scale score, 0–6) and binary (modified Rankin scale score, 0–2 versus 3–6) functional outcome was assessed using discrimination and calibration measures like area under the receiver operating characteristic curve and accuracy (percentage of correctly classified patients). RESULTS: In the cross validation, the model based on clinical variables and diffusion weighted imaging achieved the highest binary prediction performance (area under the receiver operating characteristic curve, 0.766 [0.727–0.803]). Performance of models using clinical variables or diffusion weighted imaging only was lower. Adding perfusion weighted imaging did not improve outcome prediction. On the test set of 50 patients, binary prediction performance between model (accuracy, 60% [55.4%–64.4%]) and neurologists (accuracy, 60% [55.8%–64.21%]) was similar when using clinical data. However, models significantly outperformed neurologists when imaging data were provided, alone or in combination with clinical variables (accuracy, 72% [67.8%–76%] versus 64% [59.8%–68.4%] with clinical and imaging data). Prediction performance of neurologists with comparable experience varied strongly. CONCLUSIONS: We hypothesize that early prediction of functional outcome in large vessel occlusion stroke patients may be significantly improved if neurologists are supported by interpretable deep learning models. |
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| ISSN: | 15244628 00392499 |
| DOI: | 10.1161/strokeaha.123.042496 |