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.

Level II lateral neck dissection for papillary thyroid carcinoma: A retrospective cohort study.

BACKGROUND: For N1b papillary thyroid carcinoma (PTC) patients, lateral neck dissection encompassing levels Ⅱ-Ⅴ is generally recommended. However, routine level Ⅱ dissection is controversial given the low incidence of metastasis, and potential complications such as increased shoulder syndrome. METHODS: Retrospective analysis of consecutive patients with papillary thyroid carcinoma who underwent lateral neck dissection at a single institution from January 2019 to April 2021 was performed. Clinicopathological features such as age, gender, tumor location, tumor size, TgAb and TPOAb levels, capsular invasion, multifocality and lymph node metastases were examined to evaluate the occurrence of metastatic Level Ⅱ lymph nodes. RESULTS: Overall and occult level Ⅱ metastases were observed in 51.83% and 34.84% of cN1b PTC patients. Multivariant analysis showed that primary tumor, location of primary tumor and positive level Ⅴ can serve as independent risk factors of metastasis in level Ⅱ. For cN1b PTC patients not suspected of level Ⅱ lymph nodes preoperatively, independent risk factors for predicting occult level Ⅱ metastases may include the location of primary tumor, positive level Ⅲ and positive level Ⅴ. CONCLUSION: A significant number of patients with PTC and lateral neck disease experienced Level Ⅱ metastasis, with the location of primary tumor and multilevel lymph node involvement being the independent risk factors. If the tumor is less than 1 cm and located at lower 2/3 lobe, there is minimal possibility of level Ⅱ lymph node metastasis.

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.

Mammography and ultrasound effective features in differentiating basal-like and normal-like subtypes of triple negative breast cancer.

The aim of our study was to find effective features of mammography and ultrasound in differentiating Basal-like breast cancer (BBC) and Normal-like breast cancer (NBC), two subtypes of triple negative breast cancer (TNBC). From January 2014 to March 2017, we retrospectively reviewed 91 patients who were pathologically confirmed as TNBC. According to immunohistochemical cytokeratin 5/6 (CK5/6) and Epidermal Growth Factor Receptor (EGFR), TNBCs were classified into BBCs group and NBCs group. Both CK5/6 and EGFR were negative defined to be NBC, whereas if any of CK5/6 or EGFR was positive then defined as BBC. BBCs group concluded 65 (71.4%) cases and NBCs group concluded26 (28.6%) cases. Ultrasound images and mammograms were reevaluated by breast imaging experts according to the breast imaging reporting and data system (BI-RADS) 4th edition. On mammography, masses margins had significant differences between BBCs group and NBCs group (P = 0.024). Most BBCs margins exhibited microlobulated (30/64, 46.9%) or spiculated (25/64, 39.0%), whereas most NBCs margins exhibited microlobulated (17/23, 73.9%). On ultrasound, BBCs were more frequently to present as larger than 20mm lesions (52/65, 80.0%) and more likely to have angular or spiculated margins (35/65, 53.8%), additionally, compared with NBCs, BBCs were less likely to have calcification (1/65, 1.5%). Other mammography and ultrasound features showed no significant differences between the two groups. In conclusion, we have found some effective features of mammography and ultrasound that could be helpful in differentiating BBC and NBC, which will provide some useful references for clinical diagnosis and treatment.