目的:磁共振成像对早期脑干梗死(EBI)的检测具有较高的敏感性。然而,MRI并不适用于所有可能出现中风的患者,并会导致延迟治疗。EBI在非对比计算机断层扫描(NCCT)上的检出率目前非常低。因此,我们旨在开发和验证基于影像组学特征的机器学习模型,以检测NCCT上的EBI(RMEBI).
方法:在这项回顾性观察研究中,来自华山医院建立的多中心多模式数据库的355名参与者被随机分为两个数据集:训练队列(70%)和内部验证队列(30%)。来自徐州医科大学第二附属医院的57例参与者作为外部验证队列。由NCCT放射科医师委员会对脑干进行了分段,并自动计算了1781个影像组学特征。选择相关功能后,在训练队列中评估7种机器学习模型以预测早期脑干梗死。准确性,灵敏度,特异性,正预测值,负预测值,F1分数,和受试者工作特征曲线下面积(AUC)用于评估预测模型的性能。
结果:多层感知器(MLP)RMEBI在内部验证队列中表现出最佳性能(AUC:0.99[95%CI:0.96-1.00])。外部验证队列的AUC值为0.91(95%CI:0.82-0.98)。
结论:RMEBIs在常规临床实践中具有潜力,能够在NCCT患者中进行早期脑干梗死的准确计算机辅助诊断,这可能对减少治疗决策时间具有重要的临床价值。
结论:•RMEBIs有可能准确诊断NCCT患者的早期脑干梗死。•RMEBI适用于各种多探测器CT扫描仪。•患者治疗决策时间缩短。
OBJECTIVE: Magnetic resonance imaging has high sensitivity in detecting early brainstem infarction (EBI). However, MRI is not practical for all patients who present with possible stroke and would lead to delayed treatment. The detection rate of EBI on non-contrast computed tomography (NCCT) is currently very low. Thus, we aimed to develop and validate the radiomics feature-based machine learning models to detect EBI (RMEBIs) on NCCT.
METHODS: In this retrospective observational study, 355 participants from a multicentre multimodal database established by Huashan Hospital were randomly divided into two data sets: a training cohort (70%) and an internal validation cohort (30%). Fifty-seven participants from the Second Affiliated Hospital of Xuzhou Medical University were included as the external validation cohort. Brainstems were segmented by a radiologist committee on NCCT and 1781 radiomics features were automatically computed. After selecting the relevant features, 7 machine learning models were assessed in the training cohort to predict early brainstem infarction. Accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1-score, and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the prediction models.
RESULTS: The multilayer perceptron (MLP) RMEBI showed the best performance (AUC: 0.99 [95% CI: 0.96-1.00]) in the internal validation cohort. The AUC value in external validation cohort was 0.91 (95% CI: 0.82-0.98).
CONCLUSIONS: RMEBIs have the potential in routine clinical practice to enable accurate computer-assisted diagnoses of early brainstem infarction in patients with NCCT, which may have important clinical value in reducing therapeutic decision-making time.
CONCLUSIONS: • RMEBIs have the potential to enable accurate diagnoses of early brainstem infarction in patients with NCCT. • RMEBIs are suitable for various multidetector CT scanners. • The patient treatment decision-making time is shortened.