ArticleBMC medical imaging2020
Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters.
Article in BMC medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study.Frontiers in oncology · 2025Article
- Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.Frontiers in oncology · 2025Article
- Multi-slice computed tomography radiomics combined with serum alpha-L-fucosidase: a potential biomarker for precise identification of pleomorphic adenoma and Warthin tumor.Translational cancer research · 2024Article
- Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.Frontiers in oncology · 2024Review
- Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland.Dento maxillo facial radiology · 2023Article
- The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland.Frontiers in oncology · 2021Article
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11 authors.
Funding
Abstract
backgroundOur study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.
methodsThe study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).
resultsSignificant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001).
conclusionsBoth clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.
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