Stroke · 2023
Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.
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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
Аннотация
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.
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.