Ultrasound characteristics of breast fibromatosis mimicking carcinoma.

PURPOSE: To explore the value of ultrasound (US) characteristics in diagnosing breast fibromatosis (BF) and evaluate their differences from breast carcinoma. METHODS: A total of 121 patients with BF (n = 24, 29 lesions) or invasive ductal carcinoma (IDC) (n = 97, 102 lesions) of the breast were included. Their clinical and US findings were recorded and analyzed. RESULTS: The mean age of BF was younger than that of IDC (28.75 ± 5.55 vs. 50.19 ± 9.87, p < 0.001). The mean size of the BF was smaller than that of IDC (2.09 ± 0.91 vs. 2.71 ± 1.20, p = 0.011). Compared to IDC, BF had more frequency of posterior echo attenuation (p < 0.001), less frequency of peripheral hyperechoic halo (p = 0.002), calcification (p = 0.001), US reported axillary lymph node positive (p = 0.025), and grade 2-3 vascularity (p < 0.001). The Breast Imaging Reporting and Data System categorized BF at a lower level than IDC (p < 0.001). After adjusting for age, the peripheral hyperechoic halo, posterior echo feature, and vascularity could independently identify the differences between these two entities. CONCLUSION: Some differences were observed between BF and IDC in terms of patient age, lesion size, and US characteristics.

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.

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.

A nomogram based on ultrasound scoring system for differentiating between immunoglobulin G4-related sialadenitis and primary Sjögren syndrome.

OBJECTIVES: Accurate distinguishing between immunoglobulin G4-related sialadenitis (IgG4-RS) and primary Sjögren syndrome (pSS) is crucial due to their different treatment approaches. This study aimed to construct and validate a nomogram based on the ultrasound (US) scoring system for the differentiation of IgG4-RS and pSS. METHODS: A total of 193 patients with a clinical diagnosis of IgG4-RS or pSS treated at our institution were enrolled in the training cohort (n = 135; IgG4-RS = 28, pSS = 107) and the validation cohort (n = 58; IgG4-RS = 15, pSS = 43). The least absolute shrinkage and selection operator regression algorithm was utilized to screen the most optimal clinical features and US scoring parameters. A model for the differential diagnosis of IgG4-RS or pSS was built using logistic regression and visualized as a nomogram. The performance levels of the nomogram model were evaluated and validated in both the training and validation cohorts. RESULTS: The nomogram incorporating clinical features and US scoring parameters showed better predictive value in differentiating IgG4-RS from pSS, with the area under the curves of 0.947 and 0.958 for the training cohort and the validation cohort, respectively. Decision curve analysis demonstrated that the nomogram was clinically useful. CONCLUSIONS: A nomogram based on the US scoring system showed favourable predictive efficacy in differentiating IgG4-RS from pSS. It has the potential to aid in clinical decision-making.