Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Department of Radiology, Affiliated Hospital of Nantong University, Nantong, China.
Eur Radiol. 2024 Nov;34(11):7373-7385. doi: 10.1007/s00330-024-10802-8. Epub 2024 Jun 4.
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.
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.
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.
The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients.
The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS.
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.
我们旨在开发和验证一种基于双能 CT（DECT）图像和临床特征的放射组学列线图，以对卒中时间（TSS）进行分类，这有助于做出卒中决策。
本回顾性的三中心研究连续纳入了 2016 年 8 月至 2022 年 8 月期间接受 DECT 检查的 488 例卒中患者。根据中心，将符合条件的患者分为训练、测试和验证队列。根据估计的 TSS 阈值≤4.5 h 将患者分为两组。虚拟图像优化了早期缺血性病变的可见度，具有更高的 CT 衰减。从多能谱、碘浓度、虚拟单能谱和 DECT 重建的非对比图像中提取了 535 个放射组学特征。评估了人口统计学因素以建立临床模型。放射组学列线图是一种工具，它使用多元逻辑回归分析将 Rad 评分和临床因素结合起来对 TSS 进行分类。使用接收者操作特征（ROC）分析评估预测性能，并使用决策曲线分析（DCA）比较不同模型的临床实用性和获益。
12 个特征用于构建放射组学模型。纳入临床和放射组学特征的列线图对 TSS 具有较好的预测价值。在验证队列中，列线图的 AUC 高于仅放射组学和仅临床模型（AUC：0.936 比 0.905 比 0.824）。DCA 表明了放射组学列线图模型的临床实用性。
基于 DECT 的放射组学列线图为预测患者的 TSS 提供了一种有前景的方法。
研究结果支持使用基于 DECT 的放射组学列线图预测 TSS 的潜在临床应用。
准确确定 TSS 发作时间对于决定治疗方法至关重要。放射组学-临床列线图在预测 TSS 方面表现最佳。使用开发的模型识别不同时间发生卒中的患者有助于实现个体化管理。
