ArticleDento maxillo facial radiology2022
Diagnostic grading of parotid lesions by conventional ultrasound: a pilot study.
Article in Dento maxillo facial radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Observable Classification Patterns and Diagnostic Uncertainty in Parotid Ultrasound: A Multimethod Secondary Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Differential Diagnosis of Parotid Tumors on Ultrasound: Interobserver Variability and Examiner-Specific Decision Rules-A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.BMC oral health · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo provide a graded diagnosis of benign and malignant lesions in the parotid gland by conventional ultrasound, and thus to predict the probability of malignancy of the lesions.
methodsRetrospective analysis of conventional ultrasound images of 150 patients with parotid lesions by two observers. Parotid lesions were classified into seven patterns and then categorised into eight grades: Grade 0, unsatisfied illustration on ultrasound; Grade 1, normal parotid gland; Grade 2, definitively benign; Grade 3, probably benign; Grade 4, indeterminate; Grade 5, probably malignant; Grade 6, highly suggestive malignant and Grade 7, already had malignant diagnosis. Combined with the pathological results, the conventional ultrasound diagnostic grade of parotid lesions was evaluated for predicting the probability of malignancy.
resultsThere was excellent interobserver agreement of both readers for patterns and grades (
conclusionThe conventional ultrasound diagnostic grade of parotid lesions can be used to evaluate the risk of malignancy and will be helpful to improve the imaging diagnosis and clinical treatment.
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