Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning

Abstract

OBJECTIVEFlow diverters (FDs) are designed to occlude intracranial aneurysms (IAs) while preserving flow to essential arteries. Incomplete occlusion exposes patients to risks of thromboembolic complications and rupture. A priori assessment of FD treatment outcome could enable treatment optimization leading to better outcomes. To that end, the authors applied image-based computational analysis to clinically FD-treated aneurysms to extract information regarding morphology, pre- and post-treatment hemodynamics, and FD-device characteristics and then used these parameters to train machine learning algorithms to predict 6-month clinical outcomes after FD treatment.METHODSData were retrospectively collected for 84 FD-treated sidewall aneurysms in 80 patients. Based on 6-month angiographic outcomes, IAs were classified as occluded (n = 63) or residual (incomplete occlusion, n = 21). For each case, the authors modeled FD deployment using a fast virtual stenting algorithm and hemodynamics using image-based computational fluid dynamics. Sixteen morphological, hemodynamic, and FD-based parameters were calculated for each aneurysm. Aneurysms were randomly assigned to a training or testing cohort in approximately a 3:1 ratio. The Student t-test and Mann-Whitney U-test were performed on data from the training cohort to identify significant parameters distinguishing the occluded from residual groups. Predictive models were trained using 4 types of supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM; linear and Gaussian kernels), K-nearest neighbor, and neural network (NN). In the testing cohort, the authors compared outcome prediction by each model trained using all parameters versus only the significant parameters.RESULTSThe training cohort (n = 64) consisted of 48 occluded and 16 residual aneurysms and the testing cohort (n = 20) consisted of 15 occluded and 5 residual aneurysms. Significance tests yielded 2 morphological (ostium ratio and neck ratio) and 3 hemodynamic (pre-treatment inflow rate, post-treatment inflow rate, and post-treatment aneurysm averaged velocity) discriminants between the occluded (good-outcome) and the residual (bad-outcome) group. In both training and testing, all the models trained using all 16 parameters performed better than all the models trained using only the 5 significant parameters. Among the all-parameter models, NN (AUC = 0.967) performed the best during training, followed by LR and linear SVM (AUC = 0.941 and 0.914, respectively). During testing, NN and Gaussian-SVM models had the highest accuracy (90%) in predicting occlusion outcome.CONCLUSIONSNN and Gaussian-SVM models incorporating all 16 morphological, hemodynamic, and FD-related parameters predicted 6-month occlusion outcome of FD treatment with 90% accuracy. More robust models using the computational workflow and machine learning could be trained on larger patient databases toward clinical use in patient-specific treatment planning and optimization.

Keywords: AR = aspect ratio; AUC = area under the ROC curve; AV = averaged velocity; CFD = computational fluid dynamics; DSA = digital subtraction angiography; FD = flow diverter; IA = intracranial aneurysm; ICA = internal carotid artery; IR = inflow rate; K-NN = K-nearest neighbor; LR = logistic regression; MCR = metal coverage rate; ML = machine learning; ND = neck diameter; NN = neural network; NR = neck ratio; OsR = ostium ratio; PD = pore density; PED = Pipeline embolization device; Pipeline embolization device; ROC = receiver operating characteristic; SE = standard error; SHR = shear rate; SR = size ratio; SVM = support vector machine; TT = turnover time; computational fluid dynamics; flow diverter; intracranial aneurysm; machine learning; predictive models.

Fig. 1.

Illustration of different ML classification algorithms. A: Logistic regression where a classification line is fitted on the data. B: Support vector machine, where the best hyper-plane that separates the data is identified by maximizing the margins on its either sides. C: K-nearest neighbor, where the predictions of a new point are made based on its distance from the points in the exiting database. D: Neural network with 2 hidden layers, where a system of interconnected neurons uses back-propagation to learn from the training data.

Fig. 2.

Flowchart for building and testing the predictive models for occlusion outcome of FD-treated IAs. After extraction of morphological, FD-related, and hemodynamic parameters, patients are randomly divided into the training and testing cohorts. Two sets of models are then trained using: 1) all parameters and 2) significant parameters on the training cohort. The predictive performance of these models is then tested on the testing cohort.

Fig. 3.

ROC curves for each ML model on the training cohort: built using all parameters (A) and built using significant parameters (B). AUC = area under the ROC curve; G-SVM = Gaussian support vector machine; K-NN: k-nearest neighbor; LR = logistic regression; L-SVM = linear support vector machine; NN = neural network.

Fig. 4.

Confusion matrix for predictions of ML models (trained using all parameters) on the testing cohort. The vertical axis represents the actual clinical outcome and the horizontal axis represents the model-predicted outcome. Green indicates correct predictions; red indicates incorrect predictions.

Fig. 5.

Concept of the methodology of generating clinically practical predictive models for FD-treated IAs. Left panel shows two representative patient-specific ICA aneurysms, which were treated using a single FD. Right panel shows the 6 month-occlusion outcome of both cases: top aneurysm was complete occluded (red circle) while bottom aneurysm had persistent residual filling in the IA. The middle panel show the computational analysis workflow, which uses pre-treatment 3D DSA images to generate 3D aneurysm model, and then models the FD deployment and hemodynamics using virtual stenting and CFD, respectively. The morphological, hemodynamic and candidate FD characteristics ae used as features to train machine-learning algorithms that can predict the 6-month occlusion outcome of FD-treated IAs. Note that neural networks are shown as a representative machine-learning algorithm for predictive model-building.