A DWI-based radiomics-clinical machine learning model to preoperatively predict the futile recanalization after endovascular treatment of acute basilar artery occlusion patients.

Prediction of malignant middle cerebral artery infarction (MMI) could identify patients for early intervention. We trained and internally validated a ML model that predicts MMI following mechanical thrombectomy (MT) for ACLVO.All patients who underwent MT for ACLVO between 2015 - 2021 at a single institution were reviewed. Data was divided into 80% training and 20% test sets. 10 models were evaluated on the training set. The top 3 models underwent hyperparameter tuning using grid search with nested 5-fold CV to optimize the area under the receiver operating curve (AUROC). Tuned models were evaluated on the test set and compared to logistic regression.A total of 381 patients met the inclusion criteria. There were 50 (13.1%) patients who developed MMI. Out of the 10 ML models screened on the training set, the top 3 performing were neural network (median AUROC 0.78, IQR 0.72 - 0.83), support vector machine ([SVM] median AUROC 0.77, IQR 0.72 - 0.83), and random forest (median AUROC 0.75, IQR 0.68 - 0.81). On the test set, random forest (median AUROC 0.78, IQR 0.73 - 0.83) and neural network (median AUROC 0.78, IQR 0.73 - 0.83) were the top performing models, followed by SVM (median AUROC 0.77, IQR 0.70 - 0.83). These scores were significantly better than those for logistic regression (AUROC 0.72, IQR 0.66 - 0.78), individual risk factors, and the Malignant Brain Edema score (p < 0.001 for all).ML models predicted MMI with good discriminative ability. They outperformed standard statistical techniques and individual risk factors. 

Machine learning prediction of malignant middle cerebral artery infarction after mechanical thrombectomy for anterior circulation large vessel occlusion.

PURPOSE
Various studies have identified prognostic factors for a favorable outcome of endovascular treatment in posterior circulation. We evaluated various machine learning algorithms in their ability to classify between patients with favorable (defined as 0-2 points on the modified Rankin scale [mRS]), unfavorable (mRS 3-6), poor (mRS 5-6), and nonpoor (mRS 0-4) outcomes at dismissal.


METHODS
We retrospectively analyzed data from 415 patients that were treated between 2018 and 2021 from the multicentric DGNR registry. Five models (random forest, support vector machine, k-nearest neighbor, neural network [NN], and generalized linear model [GLM]) were trained with clinical input variables and evaluated with a test dataset of 82 patients. The model with the highest accuracy on the training dataset was defined as the best model.


RESULTS
A total of 132 patients showed poor and 162 patients showed favorable outcome. All baseline variables except sex were highly significantly different between patients with favorable and unfavorable outcomes. The variables NIHSS, the presence of wake-up stroke, the administration of IV-thrombolysis and mRS pretreatment were significantly different between patients with poor and nonpoor outcomes. The best-performing NN achieved a sensitivity of 0.56, a specificity of 0.86 and an area under the curve (AUC) of 0.77 on the test dataset in the classification analysis between favorable and unfavorable outcomes. The best-performing GLM achieved a sensitivity of 0.65, a specificity of 0.91 and an AUC of 0.81 in the classification analysis between poor and nonpoor outcomes.


CONCLUSION
Short-term favorable and poor outcomes in patients with acute ischemic stroke of the posterior circulation can be predicted prior to thrombectomy with moderate sensitivity and high specificity with machine learning models.

Outcome prediction prior to thrombectomy of the posterior circulation with machine learning.

PurposeClinical outcomes vary considerably among individuals with vessel occlusion of the posterior circulation. In the present study we evaluated machine learning algorithms in their ability to di...

Hendrik Ochmann

Papers

Value of machine learning to predict functional outcome of endovascular treatment for acute ischaemic stroke of the posterior circulation.