ArticleDento maxillo facial radiology2024
An ultrasound-based histogram analysis model for prediction of tumour stroma ratio in pleomorphic adenoma of the salivary gland.
Article in Dento maxillo facial radiology, 2024. 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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3 citing papers in PubMed, 4 citations in OpenAlex.
- Development of a risk model for predicting cervical lymph node metastasis in major salivary gland carcinomas utilizing clinicopathological and ultrasound features.BMC oral health · 2025Article
- Machine learning model for preoperative classification of stromal subtypes in salivary gland pleomorphic adenoma based on ultrasound histogram analysis.BMC oral health · 2025Article
- Trends in dentomaxillofacial radiology.World journal of radiology · 2025Review
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9 authors at 1 institution in 1 country.
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Abstract
objectivesPreoperative identification of different stromal subtypes of pleomorphic adenoma (PA) of the salivary gland is crucial for making treatment decisions. We aimed to develop and validate a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumour stroma ratio (TSR) in salivary gland PA.
methodsA total of 219 PA patients were divided into low-TSR (stroma-low) and high-TSR (stroma-high) groups and enrolled in a training cohort (n = 151) and a validation cohort (n = 68). The least absolute shrinkage and selection operator regression algorithm was used to screen the most optimal clinical, US, and HA features. The selected features were entered into multivariable logistic regression analyses for further selection of independent predictors. Different models, including the nomogram model, the clinic-US (Clin + US) model, and the HA model, were built based on independent predictors using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts.
resultsLesion size, shape, cystic areas, vascularity, HA_mean, and HA_skewness were identified as independent predictors for constructing the nomogram model. The nomogram model incorporating the clinical, US, and HA features achieved areas under the curve of 0.839 and 0.852 in the training and validation cohorts, respectively, demonstrating good predictive performance and calibration. Decision curve analysis and clinical impact curves further confirmed its clinical usefulness.
conclusionsThe nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.
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