ArticleFrontiers in oncology2022
Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- An integrated clinical and imaging-based machine learning model to determine the preoperative nature of primary operable parotid tumors.BMC oral health · 2026Article
- 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
- Radiomics analysis of parotid gland tumors using DWI and DCE-MRI: comparison of multiple machine learning models for differential diagnosis.Polish journal of radiology · 2026Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Classification of Parotid Tumors with Robust Radiomic Features from DCE- and DW-MRI.Journal of imaging · 2025Article
- Clinical value of intratumoral and peritumoral CT radiomics models for discriminating benign and malignant parotid gland tumors.Frontiers in oncology · 2025Article
- Modified buried vertical mattress suture technique for excisional suturing of benign parotid tumors: A retrospective study.Journal of cosmetic dermatology · 2024Article
- The value of T1- and FST2-Weighted-based radiomics nomogram in differentiating pleomorphic adenoma and Warthin tumor.Translational oncology · 2024Article
- Article
- Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.Frontiers in oncology · 2024Review
- Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study.Frontiers in oncology · 2024Article
- Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.Frontiers in oncology · 2024Article
- Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review.Cancers · 2023Review
- The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors.Cancers · 2023Article
- CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors.Frontiers in oncology · 2023Article
- Interpretable Deep Learning Radiomics for Differentiating Pleomorphic Adenoma and Warthin Tumor.In vivo (Athens, Greece)Article
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11 authors.
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Abstract
Objectives: We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs). Materials and Methods: This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong's test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models. Results: The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT Conclusions: Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.
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