Related Experiment Video
Updated: May 21, 2025
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
Published on: July 29, 2013
Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor
Zhiwei Gong1, Jianying Li2, Yilin Han1
1Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China.
Introduction:
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.
Methods:
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).
Results:
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.
Discussion:
The nomogram combining DECT features and radiomics offers a promising non-invasive tool for differentiating PA and WT in clinical practice.
