Predictive Model for the Diagnosis of Benign/Malignant Complex Cystic and Solid Breast Nodules.

PURPOSE: To develop an ultrasound predictive model to differentiate between benign and malignant complex cystic and solid nodules (C-SNs). METHODS: A total of 211 patients with complex C-SNs rated as American College of Radiology Breast Imaging Reporting and Data System (ACR BI-RADS) category 4 or 5 on the ultrasound reports were included in the study, from June 2018-2021. Multivariate stepwise logistic regression analysis was used to establish a predictive model, based on clinical and ultrasound features. The diagnostic performance of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: A total of 109 breast nodules, including 74 benign nodules (67.89%) and 35 malignant nodules (32.11%), were detected by surgical pathology or puncture biopsy. Multivariate analysis showed that the blood flow (BF) of complex C-SNs (p = 0.03), cystic fluid transmission (p = 0.02), longitudinal diameter (p < 0.001), and age (p = 0.03) were independent risk factors for malignant complex cystic and solid breast nodules. The ultrasound model equation was Z=-12.14+2.24×X12+1.97×X20+0.40×X7+0.11×X0; M=ez1+ez (M is the malignancy score, e = 2.72). The area under the curve (AUC) was 0.89, which indicated good predictive utility for the model. CONCLUSIONS: A prediction model incorporating major risk factors can predict the malignant C-SNs with accuracy.

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images.

This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.

Author Correction: Diagnostic value of ultrasound features and sex of fetuses in female patients with papillary thyroid microcarcinoma.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Diagnostic value of ultrasound features and sex of fetuses in female patients with papillary thyroid microcarcinoma.

Little work has been done on the prediction of papillary thyroid microcarcinoma in female patients who have given birth to children, which may be different from other people. We performed a retrospective review of female patients who underwent thyroidectomy, aiming at identifying special predictors of papillary thyroid microcarcinoma in female patients who have given birth to children. Univariate analysis was used to identify potential covariates for the prediction of papillary thyroid microcarcinoma. Multivariable logistic regression analysis was used to identify independent predictors and construct a regression model based on a training cohort (246 patients) and then the regression model was validated using an independent cohort (80 patients). We found that having not more than one boy, taller-than-wide shape, poorly defined margin, marked hypoechogenicity, and microcalcification were independent risk factors for the papillary thyroid microcarcinoma in multivariate analyses. The combined predictive formula had a high predictive effect for papillary thyroid microcarcinoma (AUC = 0.938 for training cohort and 0.929 for validation cohort, respectively). The combined predictive formula has clinical value in the prognosis of papillary thyroid microcarcinoma and it may be simple and effective to ask fertility condition of patients to increase the US diagnosis accuracy of papillary thyroid microcarcinoma.