A ultrasound-based histogram analysis model for prediction of tumor stroma ratio in pleomorphic adenoma of the salivary gland.

OBJECTIVES: Preoperative identification of different stromal subtypes of pleomorphic adenoma (PA) of the salivary gland is crucial for making treatment decisions. We aimed to developed and validated a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumor stroma ratio (TSR) in SPA.

METHODS: A 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.

RESULTS: Lesion 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 AUCs 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.

CONCLUSIONS: The nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.