Gong Zhiwei, Li Jianying, Han Yilin, Chen Shiyu, Wang Lijun
Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
CT Imaging Research Center, GE Healthcare, Shanghai, China.
Front Oncol. 2025 Mar 4;15:1505385. doi: 10.3389/fonc.2025.1505385. eCollection 2025.
Accurate differentiation between pleomorphic adenomas (PA) and Warthin tumors (WT) in the parotid gland is challenging owing to overlapping imaging features. This study aimed to evaluate a nomogram combining dual-energy computed tomography (DECT) quantitative parameters and radiomics to enhance diagnostic precision.
This retrospective study included 120 patients with pathologically confirmed PA or WT, randomly divided into training and test sets (7:3). DECT features, including tumor CT values from 70 keV virtual monochromatic images (VMIs), iodine concentration (IC), and normalized IC (NIC), were analyzed. Independent predictors were identified via logistic regression. Radiomic features were extracted from segmented regions of interest and filtered using the K-best and least absolute shrinkage and selection operator. Radiomic models based on 70 keV VMIs and material decomposition images were developed using logistic regression (LR), support vector machine (SVM), and random forest (RF). The best-performing radiomics model was combined with independent DECT predictors to construct a model and nomogram. Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).
IC (venous phase), NIC (arterial phase), and NIC (venous phase) were independent DECT predictors. The DECT feature model achieved AUCs of 0.842 and 0.853 in the training and test sets, respectively, outperforming the traditional radiomics model (AUCs 0.836 and 0.834, respectively). The DECT radiomics model using arterial phase water-based images with LR showed improved performance (AUCs 0.883 and 0.925). The combined model demonstrated the highest discrimination power, with AUCs of 0.910 and 0.947. The combined model outperformed the DECT features and conventional radiomics models, with AUCs of 0.910 and 0.947, respectively (P<0.05). While the difference in AUC between the combined model and the DECT radiomics model was not statistically significant (P>0.05), it showed higher specificity, accuracy, and precision. DCA found that the nomogram gave the greatest net therapeutic effect across a broad range of threshold probabilities.
The nomogram combining DECT features and radiomics offers a promising non-invasive tool for differentiating PA and WT in clinical practice.
由于腮腺多形性腺瘤（PA）和沃辛瘤（WT）的影像学特征重叠，准确区分两者具有挑战性。本研究旨在评估一种结合双能计算机断层扫描（DECT）定量参数和放射组学的列线图，以提高诊断准确性。
本回顾性研究纳入120例经病理证实为PA或WT的患者，随机分为训练集和测试集（7:3）。分析DECT特征，包括70 keV虚拟单色图像（VMI）的肿瘤CT值、碘浓度（IC）和标准化IC（NIC）。通过逻辑回归确定独立预测因素。从分割的感兴趣区域提取放射组学特征，并使用K最优和最小绝对收缩与选择算子进行筛选。基于70 keV VMI和物质分解图像，采用逻辑回归（LR）、支持向量机（SVM）和随机森林（RF）建立放射组学模型。将性能最佳的放射组学模型与独立的DECT预测因素相结合，构建模型和列线图。使用ROC曲线、校准曲线和决策曲线分析（DCA）评估模型性能。
IC（静脉期）、NIC（动脉期）和NIC（静脉期）是独立的DECT预测因素。DECT特征模型在训练集和测试集的AUC分别为0.842和0.853，优于传统放射组学模型（AUC分别为0.836和0.834）。使用动脉期水基图像和LR的DECT放射组学模型性能有所提高（AUC分别为0.883和0.925）。联合模型显示出最高的鉴别力，AUC分别为0.910和0.947。联合模型优于DECT特征模型和传统放射组学模型，AUC分别为0.910和0.947（P<0.05）。虽然联合模型与DECT放射组学模型的AUC差异无统计学意义（P>0.05），但其具有更高的特异性、准确性和精确性。DCA发现，列线图在广泛的阈值概率范围内具有最大的净治疗效果。
结合DECT特征和放射组学的列线图为临床实践中区分PA和WT提供了一种有前景的非侵入性工具。
