Ozkara Burak B, Karabacak Mert, Hamam Omar, Wang Richard, Kotha Apoorva, Khalili Neda, Hoseinyazdi Meisam, Chen Melissa M, Wintermark Max, Yedavalli Vivek S
Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX 77030, USA.
Department of Neurosurgery, Mount Sinai Health System, New York, NY 10029, USA.
J Clin Med. 2023 Jan 20;12(3):839. doi: 10.3390/jcm12030839.
At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.
目前，临床医生需要处理大量复杂的临床、实验室和影像数据，这就需要采用复杂的分析方法。基于机器学习的模型可以利用这些海量数据来创建预测模型。我们旨在使用以临床、实验室和定量影像数据为输入的机器学习模型，预测大脑中动脉（MCA）近端闭塞的急性缺血性卒中（AIS）患者的短期和中期功能结局。纳入的患者为连续的MCA M1和近端M2闭塞的AIS患者。使用XGBoost、LightGBM、CatBoost和随机森林来预测结局。采用最小冗余最大相关性方法进行特征选择。主要结局为90天时的美国国立卫生研究院卒中量表（NIHSS）变化和改良Rankin量表（mRS）评分。在预测90天时的良好和不良结局组方面，受试者操作特征曲线下面积（AUROC）最高的算法是LightGBM。基于NIHSS变化预测良好和不良组时，随机森林的AUROC最高。结合临床、实验室和影像参数以及机器学习，我们准确预测了MCA近端闭塞的AIS患者的功能结局。
