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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Head and Neck Cancer
Volume 15 - 2025 |
doi: 10.3389/fonc.2025.1505385
This article is part of the Research Topic Advancing Cancer Imaging Technologies: Bridging the Gap from Research to Clinical Practice View all 11 articles
Nomogram Combining Dual-energy Computed Tomography Features and Radiomics for Differentiating Parotid Warthin Tumor from Pleomorphic Adenoma: A Retrospective Study
Provisionally accepted- 1 Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- 2 GE Healthcare, Beijing, China
- 3 First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning Province, 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.
Keywords: Parotid tumor, Dual-energy computed tomography, Radiomics, machine learning, combined nomogram, IDENTIFICATION
Received: 02 Oct 2024; Accepted: 27 Jan 2025.
Copyright: © 2025 Gong, LI, Han, Chen and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Lijun Wang, First Affiliated Hospital, Dalian Medical University, Dalian, 116011, Liaoning Province, China
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