Ozkara Burak B, Karabacak Mert, Hoseinyazdi Meisam, Dagher Samir A, Wang Richard, Karadon Sadik Y, Ucisik F Eymen, Margetis Konstantinos, Wintermark Max, Yedavalli Vivek S
Department of Neuroradiology, MD Anderson Cancer Center, Houston, Texas, USA.
Department of Neurosurgery, Mount Sinai Health System, New York, New York, USA.
J Neuroimaging. 2024 May-Jun;34(3):356-365. doi: 10.1111/jon.13194. Epub 2024 Mar 2.
We aimed to predict the functional outcome of acute ischemic stroke patients with anterior circulation large vessel occlusions (LVOs), irrespective of how they were treated or the severity of the stroke at admission, by only using imaging parameters in machine learning models.
Consecutive adult patients with anterior circulation LVOs who were scanned with CT angiography (CTA) and CT perfusion were queried in this single-center, retrospective study. The favorable outcome was defined as a modified Rankin score (mRS) of 0-2 at 90 days. Predictor variables included only imaging parameters. CatBoost, XGBoost, and Random Forest were employed. Algorithms were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), accuracy, Brier score, recall, and precision. SHapley Additive exPlanations were implemented.
A total of 180 patients (102 female) were included, with a median age of 69.5. Ninety-two patients had an mRS between 0 and 2. The best algorithm in terms of AUROC was XGBoost (0.91). Furthermore, the XGBoost model exhibited a precision of 0.72, a recall of 0.81, an AUPRC of 0.83, an accuracy of 0.78, and a Brier score of 0.17. Multiphase CTA collateral score was the most significant feature in predicting the outcome.
Using only imaging parameters, our model had an AUROC of 0.91 which was superior to most previous studies, indicating that imaging parameters may be as accurate as conventional predictors. The multiphase CTA collateral score was the most predictive variable, highlighting the importance of collaterals.
我们旨在通过在机器学习模型中仅使用影像参数，预测前循环大血管闭塞（LVO）急性缺血性卒中患者的功能结局，无论其治疗方式或入院时卒中严重程度如何。
在这项单中心回顾性研究中，对连续接受CT血管造影（CTA）和CT灌注扫描的前循环LVO成年患者进行了查询。良好结局定义为90天时改良Rankin量表（mRS）评分为0 - 2分。预测变量仅包括影像参数。采用了CatBoost、XGBoost和随机森林算法。使用受试者操作特征曲线下面积（AUROC）、精确召回率曲线下面积（AUPRC）、准确率、Brier评分、召回率和精确率对算法进行评估。实施了SHapley加性解释法。
共纳入180例患者（102例女性），中位年龄为69.5岁。92例患者的mRS在0至2分之间。就AUROC而言，最佳算法是XGBoost（0.91）。此外，XGBoost模型的精确率为0.72，召回率为0.81，AUPRC为0.83，准确率为0.78，Brier评分为0.17。多期CT侧支循环评分是预测结局的最显著特征。
仅使用影像参数，我们的模型AUROC为0.91，优于大多数先前研究，表明影像参数可能与传统预测指标一样准确。多期CT侧支循环评分是最具预测性的变量，突出了侧支循环的重要性。
