Abstract：
OBJECTIVE: We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making.
METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models.
RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model.
CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients.
CONCLUSIONS: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS.
CONCLUSIONS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.
METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models.
RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model.
CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients.
CONCLUSIONS: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS.
CONCLUSIONS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.
摘要：
目的：我们旨在开发和验证基于双能计算机断层扫描（DECT）图像和临床特征的放射组学列线图，以对中风后时间（TSS）进行分类。这可以促进中风决策。
方法：这项回顾性三中心研究连续纳入了2016年8月至2022年8月期间接受DECT的488例脑卒中患者。对符合条件的患者进行了培训，test,和根据中心的验证队列。根据估计的≤4.5h的TSS阈值将患者分为两组。虚拟图像优化了早期缺血性病变的可见性，并具有更多的CT衰减。总共从多能中提取了535个影像组学特征，碘浓度,虚拟单能量，和使用DECT重建的非造影图像。评估人口统计学因素以建立临床模型。放射组学列线图是Rad评分和临床因素使用多变量逻辑回归分析对TSS进行分类的工具。使用接收器工作特性(ROC)分析评估预测性能，和决策曲线分析（DCA）用于比较不同模型的临床效用和益处。
结果：12个特征被用于构建影像组学模型。包含临床和影像组学特征的列线图对TSS显示出良好的预测价值。在验证队列中，列线图显示AUC高于仅放射组学和仅临床模型(AUC:0.936vs0.905vs0.824).DCA证明了放射组学列线图模型的临床实用性。
结论：基于DECT的影像组学列线图为预测患者的TSS提供了一种有希望的方法。
结论：研究结果支持基于DECT的影像组学列线图预测TSS的潜在临床应用。
结论：准确确定TSS的发病对决定治疗方法至关重要。影像组学临床列线图显示了预测TSS的最佳性能。使用开发的模型来识别中风以来不同时间的患者可以促进个性化管理。
方法：这项回顾性三中心研究连续纳入了2016年8月至2022年8月期间接受DECT的488例脑卒中患者。对符合条件的患者进行了培训，test,和根据中心的验证队列。根据估计的≤4.5h的TSS阈值将患者分为两组。虚拟图像优化了早期缺血性病变的可见性，并具有更多的CT衰减。总共从多能中提取了535个影像组学特征，碘浓度,虚拟单能量，和使用DECT重建的非造影图像。评估人口统计学因素以建立临床模型。放射组学列线图是Rad评分和临床因素使用多变量逻辑回归分析对TSS进行分类的工具。使用接收器工作特性(ROC)分析评估预测性能，和决策曲线分析（DCA）用于比较不同模型的临床效用和益处。
结果：12个特征被用于构建影像组学模型。包含临床和影像组学特征的列线图对TSS显示出良好的预测价值。在验证队列中，列线图显示AUC高于仅放射组学和仅临床模型(AUC:0.936vs0.905vs0.824).DCA证明了放射组学列线图模型的临床实用性。
结论：基于DECT的影像组学列线图为预测患者的TSS提供了一种有希望的方法。
结论：研究结果支持基于DECT的影像组学列线图预测TSS的潜在临床应用。
结论：准确确定TSS的发病对决定治疗方法至关重要。影像组学临床列线图显示了预测TSS的最佳性能。使用开发的模型来识别中风以来不同时间的患者可以促进个性化管理。
