ArticleFrontiers in oncology2022
Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors.
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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Who cites it
17 citing papers in PubMed.
- The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors.Scientific reports · 2026Article
- Could a multimodal fusion model integrating CT radiomics and systemic inflammatory markers improve preoperative risk stratification of parotid masses? A retrospective exploratory study.Frontiers in immunology · 2026Article
- Screening opportunistic osteoporosis through multimodal techniques of hip joint CT images: exploring 2D and 3D deep learning, radiomics, clinical data, and their integration.Frontiers in endocrinology · 2026Article
- Deep-learning-based radiomics of intratumoral and peritumoral CT images to differentiate benign from malignant parotid tumors.American journal of cancer research · 2026Article
- Non-invasive prediction of DCE-MRI radiomics model on CCR5 in breast cancer based on a machine learning algorithm.Cancer biomarkers : section A of Disease markers · 2025Article
- Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.Frontiers in oncology · 2025Article
- Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study.Frontiers in oncology · 2025Article
- Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer.Heliyon · 2024Article
- Article
- Multispectral optoacoustic tomography of benign parotid tumors in vivo: a prospective observational pilot study.Scientific reports · 2024Observational
- 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
- Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.Frontiers in oncology · 2024Review
- Preoperative CT-based intra- and peri-tumoral radiomic models for differentiating benign and malignant tumors of the parotid gland: a two-center study.American journal of cancer research · 2024Article
- Article
- Salivary gland pathologies: evolution in classification and association with unique genetic alterations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Review
- Deep learning-assisted diagnosis of benign and malignant parotid tumors based on contrast-enhanced CT: a multicenter study.European radiology · 2023Article
- Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling.Military Medical Research · 2023Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs). Methods: This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training ( Results: Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, Conclusion: Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.
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