Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning
Outcome prediction of intracranial aneurysm treatment by flow diverters using machine learning
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DOI：
10.3171/2018.8.focus18332
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发表时间：
2018-11-01
影响因子：
4.1
通讯作者：
Meng, Hui
中科院分区：
文献类型：
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作者：
Paliwal, Nikhil;Jaiswal, Prakhar;Meng, Hui
目的 分流器 (FD) 旨在闭塞颅内动脉瘤 (LA)，同时保留主要动脉的血流。不完全闭塞使患者面临血栓栓塞并发症和破裂的风险。对 FD 治疗结果的先验评估可以优化治疗，从而获得更好的结果。为此，作者将基于图像的计算分析应用于临床 FD 治疗的动脉瘤，以提取有关形态、治疗前后血流动力学和 FD 设备特征的信息，然后使用这些参数训练机器学习算法以预测 FD 治疗后 6 个月的临床结果。 方法 回顾性收集 84 个 FD 治疗的侧壁动脉瘤的数据 在 80 名患者中。根据 6 个月血管造影结果，IAs 被分类为闭塞 (n = 63) 或残留 (不完全闭塞，n = 21)。对于每种情况，作者使用快速虚拟支架算法和基于图像的计算流体动力学的血流动力学对 FD 部署进行了建模。对每个动脉瘤计算了 16 个形态学、血流动力学和基于 FD 的参数。动脉瘤以大约 3:1 的比例随机分配到训练组或测试组。对来自训练队列的数据进行学生 t 检验和曼-惠特尼 U 检验，以确定区分遮挡组和剩余组的重要参数。使用 4 种监督机器学习算法训练预测模型：逻辑回归 (LR)、支持向量机（SVM；线性和高斯核）、K 最近邻和神经网络 (NN)。在测试队列中，作者比较了使用所有参数与仅使用重要参数训练的每个模型的结果预测。 结果 训练队列 (n = 64) 由 48 个闭塞动脉瘤和 16 个残余动脉瘤组成，测试队列 (n = 20) 由 15 个闭塞动脉瘤和 5 个残余动脉瘤组成。显着性测试得出了闭塞组（好结果）和残留组（坏结果）之间的 2 个形态学（开口比和颈比）和 3 个血流动力学（治疗前流入率、治疗后流入率和治疗后动脉瘤平均速度）判别因素。在训练和测试中，使用所有 16 个参数训练的所有模型都比仅使用 5 个重要参数训练的所有模型表现更好。在全参数模型中，NN（AUC = 0.967）在训练过程中表现最好，其次是 LR 和线性 SVM（AUC = 0.941 和 0.914）。在测试过程中，NN 和 Gaussian-SVM 模型在预测闭塞结果方面具有最高的准确度 (90%)。结论 NN 和 Gaussian-SVM 模型结合了所有 16 个形态学、血流动力学和 FD 相关参数，预测 FD 治疗 6 个月的闭塞结果，准确度为 90%。使用计算工作流程和机器学习的更强大的模型可以在更大的患者数据库上进行训练，以便临床用于患者特定的治疗计划和优化。
OBJECTIVE Flow diverters (FDs) are designed to occlude intracranial aneurysms (lAs) 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.METHODS Data 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.RESULTS The 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.CONCLUSIONS NN 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.