Prediction of malignant risk stratification model for parotid gland nodules based on clinical and conventional ultrasound features: construction and validation.

Background: Ultrasound is widely used in the examination of the parotid gland, but no single ultrasound feature has demonstrated satisfactory diagnostic performance in predicting the nature of parotid nodules. Unlike the established and widely used grading systems for breast and thyroid nodules, a universally adopted and clinically accepted risk stratification system for malignancy in parotid gland nodules remains absent at present. This study aims to establish a malignant risk stratification model for parotid nodules by analyzing patients' clinical features and conventional ultrasound image characteristics. Methods: In this study, clinical data and ultrasound images of 736 patients with parotid nodules were retrospectively analyzed. Pathological results served as the gold standard, and the patients were randomly divided into training and validation groups in a 7:3 ratio. Clinical and ultrasound features of parotid nodules in the training group were compared. Multifactor logistic regression analysis was employed to screen for risk factors of malignant nodules and quantify scores. The probability of malignant risk was assessed and classified into five grades (Grade 1, normal parotid; Grade 2, definitive benign; Grade 3, possibly benign; Grade 4, suspicious malignant; Grade 5, high probability of malignancy). The diagnostic performance of the model was assessed by using calibration curves, receiver operating characteristic curves, decision curves, and clinical impact curves. Results: Facial symptoms, unclear margin, irregular shape, microcalcification, and abnormal cervical lymph nodes were independent risk factors for malignant parotid nodules. The area under the curve of the model was 0.850 [95% confidence interval (CI): 0.816-0.879] in the training group and 0.846 (95% CI: 0.791-0.891) in the validation group. Conclusions: The malignancy risk stratification model based on clinical and ultrasound image features has a good differentiation between benign and malignant parotid nodules, which is helpful for diagnosis and guiding clinical treatment.