Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province 030001, PR China.
J Stroke Cerebrovasc Dis. 2023 Aug;32(8):107209. doi: 10.1016/j.jstrokecerebrovasdis.2023.107209. Epub 2023 Jun 7.
This study aimed to develop and validate a machine learning model incorporating both dual-energy computed tomography (DECT) angiography quantitative parameters and clinically relevant risk factors for the identification of symptomatic carotid plaques to prevent acute cerebrovascular events.
The data of 180 patients with carotid atherosclerosis plaques were analysed from January 2017 to December 2021; 110 patients (64.03±9.58 years old, 20 women, 90 men) were allocated to the symptomatic group, and 70 patients (64.70±9.89 years old, 50 women, 20 men) were allocated to the asymptomatic group. Overall, five machine learning models using the XGBoost algorithm, based on different CT and clinical features, were developed in the training cohort. The performances of all five models were assessed in the testing cohort using receiver operating characteristic curves, accuracy, recall rate, and F1 score.
The shapley additive explanation (SHAP) value ranking showed fat fraction (FF) as the highest among all CT and clinical features and normalised iodine density (NID) as the 10th. The model based on the top 10 features from the SHAP measurement showed optimal performance (area under the curve [AUC] .885, accuracy .833, recall rate .933, F1 score .861), compared with the other four models based on conventional CT features (AUC .588, accuracy .593, recall rate .767, F1 score .676), DECT features (AUC .685, accuracy .648, recall rate .667, F1 score .678), conventional CT and DECT features (AUC .819, accuracy .740, recall rate .867, F1 score .788), and all CT and clinical features (AUC .878, accuracy .833, recall rate .867, F1 score .852).
FF and NID can serve as useful imaging markers of symptomatic carotid plaques. This tree-based machine learning model incorporating both DECT and clinical features could potentially comprise a non-invasive method for identification of symptomatic carotid plaques to guide clinical treatment strategies.
本研究旨在开发和验证一种机器学习模型，该模型结合了双能 CT 血管造影术定量参数和临床相关危险因素，以识别有症状的颈动脉斑块，从而预防急性脑血管事件。
分析了 2017 年 1 月至 2021 年 12 月 180 例颈动脉粥样硬化斑块患者的数据；110 例患者（64.03±9.58 岁，20 名女性，90 名男性）被分配到有症状组，70 例患者（64.70±9.89 岁，50 名女性，20 名男性）被分配到无症状组。总的来说，基于不同的 CT 和临床特征，使用 XGBoost 算法在训练队列中开发了五个机器学习模型。使用受试者工作特征曲线、准确性、召回率和 F1 评分评估所有五个模型在测试队列中的性能。
Shapley 加性解释（SHAP）值排名显示，脂肪分数（FF）在所有 CT 和临床特征中最高，标准化碘密度（NID）排名第 10。基于 SHAP 测量的前 10 个特征的模型表现最佳（曲线下面积 [AUC].885，准确性.833，召回率.933，F1 得分.861），优于基于常规 CT 特征（AUC.588，准确性.593，召回率.767，F1 得分.676）、DECT 特征（AUC.685，准确性.648，召回率.667，F1 得分.678）、常规 CT 和 DECT 特征（AUC.819，准确性.740，召回率.867，F1 得分.788）和所有 CT 和临床特征（AUC.878，准确性.833，召回率.867，F1 得分.852）的四个模型。
FF 和 NID 可作为有症状颈动脉斑块的有用影像学标志物。这种基于树的机器学习模型，同时结合了 DECT 和临床特征，有可能成为一种非侵入性的方法，用于识别有症状的颈动脉斑块，以指导临床治疗策略。
