Department of Neurology, Zhuhai Hospital Affiliated with Jinan University, No. 79 Kangning Road, Zhuhai, 519000, Guangdong, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100080, China.
BMC Med Imaging. 2020 Jul 8;20(1):77. doi: 10.1186/s12880-020-00470-7.
This study aimed to investigate integrating radiomics with clinical factors in cranial computed tomography (CT) to predict ischemic strokes in patients with silent lacunar infarction (SLI).
Radiomic features were extracted from baseline cranial CT images of patients with SLI. A least absolute shrinkage and selection operator (LASSO)-Cox regression analysis was used to select significant prognostic factors based on Model with clinical factors, Model with radiomic features, and Model with both factors. The Kaplan-Meier method was used to compare stroke-free survival probabilities. A nomogram and a calibration curve were used for further evaluation.
Radiomic signature (p < 0.01), age (p = 0.09), dyslipidemia (p = 0.03), and multiple infarctions (p = 0.02) were independently associated with future ischemic strokes. Model had the best accuracy with 6-, 12-, and 18-month areas under the curve of 0.84, 0.81, and 0.79 for the training cohort and 0.79, 0.88, and 0.75 for the validation cohort, respectively. Patients with a Model score < 0.17 had higher probabilities of stroke-free survival. The prognostic nomogram and calibration curves of the training and validation cohorts showed acceptable discrimination and calibration capabilities (concordance index [95% confidence interval]: 0.7864 [0.70-0.86]; 0.7140 [0.59-0.83], respectively).
Radiomic analysis based on baseline CT images may provide a novel approach for predicting future ischemic strokes in patients with SLI. Older patients and those with dyslipidemia or multiple infarctions are at higher risk for ischemic stroke and require close monitoring and intensive intervention.
本研究旨在探讨将影像组学与临床因素相结合，应用于头颅 CT 预测无症状性腔隙性脑梗死（SLI）患者的缺血性卒中。
从 SLI 患者的基线头颅 CT 图像中提取影像组学特征。基于模型（临床因素模型、影像组学特征模型和联合因素模型），采用最小绝对收缩和选择算子（LASSO）-Cox 回归分析选择有意义的预后因素。采用 Kaplan-Meier 法比较无卒中生存概率。通过列线图和校准曲线进一步评估。
影像组学特征（p<0.01）、年龄（p=0.09）、血脂异常（p=0.03）和多发性梗死（p=0.02）与未来缺血性卒中独立相关。模型在训练队列中，6、12、18 个月的曲线下面积分别为 0.84、0.81、0.79，验证队列中分别为 0.79、0.88、0.75，具有最佳的准确性。模型评分<0.17 的患者有更高的无卒中生存概率。训练队列和验证队列的预后列线图和校准曲线显示出良好的区分度和校准能力（一致性指数[95%置信区间]：训练队列 0.7864[0.70-0.86]；验证队列 0.7140[0.59-0.83]）。
基于基线 CT 图像的影像组学分析可能为预测 SLI 患者未来的缺血性卒中提供一种新方法。年龄较大、血脂异常或多发性梗死的患者发生缺血性卒中的风险较高，需要密切监测和强化干预。
