ArticleTranslational oncology2024
The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.
Article in Translational oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Evaluation of Systemic Inflammatory Markers in Predicting Malignancy in Parotid Gland Tumors.The Laryngoscope · 2026Observational
- The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors.Scientific reports · 2026Article
- Advanced magnetic resonance neurography for preoperative facial nerve assessment and surgical planning in parotid tumors: a review of current evidence and surgical translation.Frontiers in oncology · 2026Review
- Interpretable Deep Learning Radiomics for Differentiating Pleomorphic Adenoma and Warthin Tumor.In vivo (Athens, Greece)Article
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Authors and funding
12 authors.
Funding
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Abstract
purposeTo establish a radiomics nomogram based on MRI radiomics features combined with clinical characteristics for distinguishing pleomorphic adenoma (PA) from warthin tumor (WT).
methods294 patients with PA (n = 159) and WT (n = 135) confirmed by histopathology were included in this study between July 2017 and June 2023. Clinical factors including clinical data and MRI features were analyzed to establish clinical model. 10 MRI radiomics features were extracted and selected from T1WI and FS-T2WI, used to establish radiomics model and calculate radiomics scores (Rad-scores). Clinical factors and Rad-scores were combined to serve as crucial parameters for combined model. Through Receiver operator characteristics (ROC) curve and decision curve analysis (DCA), the discriminative values of the three models were qualified and compared, the best-performing combined model was visualized in the form of a radiomics nomogram.
resultsThe combined model demonstrated excellent discriminative performance for PA and WT in the training set (AUC=0.998) and testing set (AUC=0.993) and performed better compared with the clinical model and radiomics model in the training set (AUC=0.996, 0.952) and testing model (AUC=0.954, 0.849). The DCA showed that the combined model provided more overall clinical usefulness in distinguishing parotid PA from WT than another two models.
conclusionAn analytical radiomics nomogram based on MRI radiomics features, incorporating clinical factors, can effectively distinguish between PA and WT.
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