Hu Weiming, Lin Guihan, Chen Weiyue, Wu Jianhua, Zhao Ting, Xu Lei, Qian Xusheng, Shen Lin, Yan Zhihan, Chen Minjiang, Xia Shuiwei, Lu Chenying, Yang Jing, Xu Min, Chen Weiqian, Ji Jiansong
Zhejiang Key Laboratory of Imaging and Interventional Medicine, Zhejiang Engineering Research Center of Interventional Medicine Engineering and Biotechnology, Key Laboratory of Precision Medicine of Lishui City, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, Zhejiang, China.
Department of Vascular Surgery, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, 323000, China.
Sci Rep. 2025 Mar 26;15(1):10415. doi: 10.1038/s41598-025-92855-3.
This study aims to create a radiomics nomogram using dual-energy computed tomography (DECT) virtual monoenergetic images (VMI) to accurately identify symptomatic carotid plaques. Between January 2018 and May 2023, data from 416 patients were collected from two centers for retrospective analysis. Center 1 provided data for the training (n = 213) and internal validation (n = 93) sets, and center 2 supplied the external validation set (n = 110). Plaques imaged at 40 keV, 70 keV, and 100 keV were outlined, and the selected radiomics features were used to establish the radiomics model. The classifier with the highest area under the curve (AUC) in the training set generated the radiomics score (Rad-Score). Logistic regression was used to identify risk factors and establish a clinical model. A radiomics nomogram integrating the Rad-score and clinical risk factors was constructed. The predictive performance was evaluated using receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Plaque ulceration and plaque burden are independent risk factors for symptomatic carotid plaques. The 40 + 70 keV radiomics model achieved excellent diagnostic performance, with an average AUC of 0.805 across all validation sets. Furthermore, the radiomics nomogram, integrating the Rad-score with clinical predictors, demonstrated robust diagnostic accuracy, with AUCs of 0.909, 0.850, and 0.804 in the training, internal validation, and external validation sets, respectively. DCA results suggested that the nomogram was clinically valuable. Our study developed and validated a DECT VMI-based radiomics nomogram for early identification of symptomatic carotid plaques, which can be used to assist clinical diagnosis and treatment decisions. The study introduces an innovative radiomics nomogram utilizing DECT VMI to discern symptomatic carotid plaques with high precision.
本研究旨在利用双能计算机断层扫描（DECT）虚拟单能量图像（VMI）创建一个放射组学列线图，以准确识别有症状的颈动脉斑块。2018年1月至2023年5月期间，从两个中心收集了416例患者的数据进行回顾性分析。中心1提供训练集（n = 213）和内部验证集（n = 93）的数据，中心2提供外部验证集（n = 110）的数据。勾勒出在40 keV、70 keV和100 keV下成像的斑块，并使用选定的放射组学特征建立放射组学模型。训练集中曲线下面积（AUC）最高的分类器生成放射组学评分（Rad-Score）。采用逻辑回归识别危险因素并建立临床模型。构建了一个整合Rad评分和临床危险因素的放射组学列线图。使用受试者操作特征（ROC）分析和决策曲线分析（DCA）评估预测性能。斑块溃疡和斑块负荷是有症状颈动脉斑块的独立危险因素。40 + 70 keV放射组学模型具有出色的诊断性能，所有验证集的平均AUC为0.805。此外，将Rad评分与临床预测因素相结合的放射组学列线图显示出强大的诊断准确性，在训练集、内部验证集和外部验证集中的AUC分别为0.909、0.850和0.804。DCA结果表明该列线图具有临床价值。我们的研究开发并验证了一种基于DECT VMI的放射组学列线图，用于早期识别有症状的颈动脉斑块，可用于辅助临床诊断和治疗决策。该研究引入了一种创新的放射组学列线图，利用DECT VMI高精度地识别有症状的颈动脉斑块。
