Close relationship with the glandular capsule：a highly sensitive diagnostic indicator of major salivary gland metastatic malignancies in ultrasound.

OBJECTIVES: To investigate the ultrasound (US) characteristics of metastatic malignancies (MM) in the major salivary glands and to assess the diagnostic value of the close relationship with the glandular capsule in identifying MM. METHODS: From January 2016 and April 2022, 122 patients with major salivary gland malignancies, including 20 patients with MM and 102 patients with primary malignancies (PM) confirmed by histopathological examination, were enrolled in this study. Their clinicopathologic and US data were recorded and analyzed. The diagnostic performance of the close relationship with the glandular capsule for differentiating MM from PM was analyzed. RESULTS: The mean age of MM were older than that of PM (59.50 ± 14.57 vs. 49.96 ± 15.73, p = 0.013). Compared with PM patients, MM were associated with a higher prevalence of local pain symptoms (p = 0.007) and abnormal facial nerve function (p < 0.001). MM were also more frequently characterized by unclear borders, rough margins, irregular shapes, heterogeneous internal echos, absence of cystic areas, presence of calcifications, close relationship with the glandular capsule, and US-reported positive cervical lymph nodes (all p < 0.05). The close relationship with the glandular capsule showed to be a good indicator in distinguishing between MM and PM, with an area under the receiver operating characteristic curve of 0.863, a sensitivity of 100%, a specificity of 72.5%, and an accuracy of 92.2%. Positive and negative predictive were calculated at 41.7% and 100%, respectively. CONCLUSIONS: The US finding of a close relationship with the glandular capsule is a highly sensitive diagnostic indicator for MM. Following this finding, US-guided needle biopsy should be recommended to further confirm the diagnosis.

A Nomogram Based on Conventional Ultrasound Radiomics for Differentiating Between Radial Scar and Invasive Ductal Carcinoma of the Breast.

ABSTRACT: We aimed to develop and validate a nomogram based on conventional ultrasound (CUS) radiomics model to differentiate radial scar (RS) from invasive ductal carcinoma (IDC) of the breast. In total, 208 patients with histopathologically diagnosed RS or IDC of the breast were enrolled. They were randomly divided in a 7:3 ratio into a training cohort (n = 145) and a validation cohort (n = 63). Overall, 1316 radiomics features were extracted from CUS images. Then a radiomics score was constructed by filtering unstable features and using the maximum relevance minimum redundancy algorithm and the least absolute shrinkage and selection operator logistic regression algorithm. Two models were developed using data from the training cohort: one using clinical and CUS characteristics (Clin + CUS model) and one using clinical information, CUS characteristics, and the radiomics score (radiomics model). The usefulness of nomogram was assessed based on their differentiating ability and clinical utility. Nine features from CUS images were used to build the radiomics score. The radiomics nomogram showed a favorable predictive value for differentiating RS from IDC, with areas under the curve of 0.953 and 0.922 for the training and validation cohorts, respectively. Decision curve analysis indicated that this model outperformed the Clin + CUS model and the radiomics score in terms of clinical usefulness. The results of this study may provide a novel method for noninvasively distinguish RS from IDC.

An ultrasound-based histogram analysis model for prediction of tumour 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 develop and validate a model based on histogram analysis (HA) of ultrasound (US) images for predicting tumour stroma ratio (TSR) in salivary gland PA. 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 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. CONCLUSIONS: The nomogram model we developed offers a practical tool for preoperative TSR prediction in PA, potentially enhancing clinical decision-making.