Background:Axillary lymph node metastasis (ALNM) is a major determinant of prognosis and treatment strategy in patients with breast cancer, yet reliable preoperative assessment remains challenging. Conventional ultrasound evaluation is primarily morphology-based and subject to interobserver variability. Contrast-enhanced ultrasound (CEUS) with VueBox enables the quantitative and reproducible assessment of tumor perfusion. This study aimed to develop and validate a nomogram integrating quantitative CEUS parameters with multimodal ultrasound features and serological, pathological, and immunohistochemical indicators for the preoperative prediction of ALNM. Methods:This retrospective study included 82 patients with breast cancer pathologically confirmed between January 2023 and July 2025, comprising 84 breast lesions. According to postoperative histopathological findings, patients were categorized into an ALNM-positive group (n=36) or an ALNM-negative group (n=48). Quantitative analysis of CEUS time-intensity curves (TICs) was performed with VueBox multimodal ultrasound features-including conventional two-dimensional ultrasound, color Doppler flow imaging (CDFI), shear wave elastography (SWE), and qualitative and quantitative CEUS characteristics-which were integrated with serological, pathological, and immunohistochemical indicators. Univariate and multivariate logistic regression analyses were performed to identify independent predictors. A diagnostic nomogram was constructed and evaluated with receiver operating characteristic (ROC) analysis, calibration curves, and decision curve analysis (DCA). Internal validation was conducted via bootstrap resampling with 1,000 iterations. Results:Multivariate logistic regression analysis identified four independent predictors of ALNM: maximum Young modulus [Emax; odds ratio (OR) =1.018; 95% confidence interval (CI): 1.006-1.030; P=0.004], enlarged enhancement scope (OR =10.868; 95% CI: 2.191-53.906; P=0.004), microcalcifications (OR =8.536; 95% CI: 2.092-34.832; P=0.003), and time to peak intensity (TTP; OR =0.849; 95% CI: 0.722-0.998; P=0.047). The nomogram incorporating these variables demonstrated robust discriminatory performance, with an area under the curve (AUC) of 0.926 (95% CI: 0.872-0.980), a sensitivity of 97%, and a specificity of 75%. Internal validation with bootstrap resampling yielded a concordance index (C-index) of 0.926 (95% CI: 0.865-0.974), indicating robust model stability. The calibration curve showed satisfactory agreement between predicted and observed probabilities, and the Hosmer-Lemeshow test indicated good model fit (χ2=4.19; P=0.84). DCA indicated potential net clinical benefit across a broad range of threshold probabilities. Conclusions:The column nomogram model developed in this study, based on quantitative analysis of VueBox ultrasound contrast imaging and multimodal feature fusion, demonstrated excellent performance in predicting ALNM and holds clinical application value.

ObjectiveTo develop an ultrasound-based tumor shrinkage pattern and subsequently construct a predictive model for pathological complete response by analyzing changes in ultrasonic features and measurements at mid-treatment of neoadjuvant chemotherapy (NAC) in invasive breast cancer, ultimately evaluating its clinical applicability.MethodsThis study included all breast cancer patients who underwent NAC and subsequent surgery between January 2017 and December 2024. All patients received breast ultrasound examinations before and during NAC. All breast tumor images were reviewed by two experienced sonographers, who assessed and documented the ultrasound characteristics of the tumors. Based on the comparison of parameter changes, the tumors were classified into two patterns: centripetal shrinkage (CS) and non-centripetal shrinkage (NCS). NCS was further subdivided into four patterns: echogenic change, partial shrinkage, dendritic shrinkage, and fragmentation. According to the postoperative pathological results, patients were categorized into two groups: the pathological complete response (pCR) group and the non-pCR group. Univariate analysis and multivariate logistic regression analysis were performed to identify independent predictors for the predictive model. A nomogram was constructed, and receiver operating characteristic curves were plotted to evaluate the model’s performance.ResultsThe study included a total of 287 lesions. The results of the logistic shrinkage analysis indicated CS, ΔS(the rate of area change), echogenicity changes, Adler decrease, HER-2 positivity, and Ki-67 were identified as independent predictors of pCR (all P< 0.05). The nomogram model based on these factors demonstrated areas under the curve of 0.906 (95% CI = 0.866 - 0.946) in the training set and 0.850 (95%CI = 0.770 - 0.929) in the testing set. The univariate model using only the shrinkage pattern (CS vs NCS) yielded an AUC of 0.741 (95% CI: 0.680 - 0.803).ConclusionIt is feasible to establish tumor shrinkage patterns based on ultrasound examinations during the mid-NAC stage. The nomogram-based predictive model, which combines ultrasound image changes and tumor pathological indicators, can predict NAC outcomes and holds certain clinical application value.

BackgroundEarly detection of breast cancer and accurate assessment of lesions are key goals of imaging evaluation. Ultrasound is widely used, but its diagnostic performance is influenced by complex image features, noise, and operator experience. Reducing operator dependence and improving accuracy are critical clinical issues. MethodsIn this retrospective study, 7,025 breast ultrasound images from our center were annotated based on pathology and split into training, validation, and internal test sets (8:1:1). The Dataset of Breast Ultrasound Images was used as the external test set. YOLO-v7 and YOLO-v8 models were trained through transfer learning after data augmentation and balancing the classes. Performance was compared on internal and external test sets and was evaluated against a reader study. ResultsYOLO-v7 and YOLO-v8 reached optimal performance at epochs 294 and 135, respectively. YOLO-v7 slightly outperformed YOLO-v8 on the internal test set, while YOLO-v8 achieved higher accuracy, recall, specificity, precision, and F1 score on the external test set. Both models showed significantly higher accuracy, specificity, and precision than the senior radiologist, with YOLO-v8 achieving a significantly higher F1 score. DiscussionYOLO-v8 demonstrated better generalization due to its anchor-free mechanism and deeper architecture, while YOLO-v7 showed signs of overfitting. Both models outperformed the junior radiologist and approached or exceeded the diagnostic performance of the senior radiologist, indicating potential to assist less experienced readers. ConclusionYOLO-v7 and YOLO-v8 effectively classified breast lesions. YOLO-v8 showed faster convergence and higher diagnostic efficiency, suggesting strong potential for clinical application.

Objective: This study assessed epidemiological trends from 1990 to 2021, quantified risk factors contributions to CVD burden, and predicted risk-attributable mortality. Methods: Our study combines data from the 2021 Global Burden of Disease and the China Health and Retirement Longitudinal Study (CHARLS). Joinpoint regression analyzed trends in incidence, prevalence, mortality, and disability-adjusted life years (DALYs). Based on CHARLS database, logistic regression was used for comorbidity analysis. BAPC models projected mortality to 2036. Results: From 1990 to 2021, ASIR and ASPR increased significantly (APC = 0.12, P = 0.004; APC = 0.33, P < 0.001), while ASDR and DALYs declined (APC = −1.01, P < 0.001; APC = −1.34, P < 0.001), with males exhibiting consistently higher burden than females (P < 0.001). Among individuals aged >75 years, mortality rates exceeded 4000 per 100,000 population, and the DALYs surpassed 60,000 per 100,000 population. High systolic blood pressure was the predominant risk factor. Logistic regression analysis of CHARLS data revealed significant associations between CVD and chronic lung disease (OR = 1.78), liver disease (OR = 1.49), and kidney disease (OR = 1.85). The BAPC prediction model indicates that all CVD risk factors show declining mortality trajectories over 15 years, with sex-specific variation in contributions. Conclusions: Declining CVD mortality and rising incidence and prevalence among Chinese individuals ≥45 years marks a transition towards chronic disease management. Despite overall declines, persistently high mortality due to elevated LDL cholesterol and blood glucose necessitates targeted interventions to optimize resource allocation.

Background:To evaluate sex-related differences in the risk factors associated with nonhealing or recurrence of hyperthyroidism (NHRH) in patients with Graves' disease (GD) treated with radioactive iodine. Methods:In total, 285 patients were enrolled. Data on radioactive iodine (RAI) dosage, ultrasound indexes of the thyroid, and other clinical factors were collected. Patients were divided into NHRH and non-NHRH (hypothyroidism or euthyroidism) groups based on treatment outcomes. Univariate and multivariate weighted logistic regression analyses were used to identify factors associated with NHRH. Sex-specific analyses of these risk factors were also conducted. Results:There were no significant differences between the two groups in terms of sex, thyroid shear wave elastography velocity values, or pretreatment serum free thyroxine (FT4) levels. Thyroid volume and age were independently associated with NHRH, with the odds of NHRH gradually decreasing as age increased. In subgroup analyses, both age and thyroid volume were independent risk factors for NHRH in female patients (p < 0.05), while in male patients, only FT4 was independently associated with NHRH (p < 0.05). Conclusions:In patients of different sexes, the influence of thyroid volume, age, and FT4 on treatment outcomes exhibits distinct patterns.

This study aimed to develop an explainable machine learning framework integrating dual-modality ultrasonography and thyroid function parameters for preoperative prediction of central lymph node metastasis (CLNM) in capsular-invasive papillary thyroid carcinoma. A retrospective cohort of 382 pathologically confirmed capsular-invasive papillary thyroid carcinoma patients was stratified into CLNM-negative and CLNM-positive cohorts. After comprehensive univariate and multivariate logistic regression analyses, predictive models were developed using 8 machine learning algorithms (including Logistic Regression, Support Vector Machine, Gradient Boosting Machine, eXtreme Gradient Boosting, K-Nearest Neighbors, Adaptive Boosting, Neural Network, and Categorical Boosting [CatBoost]) and rigorously validated through receiver operating characteristic analysis. Multivariate analysis showed irregular margins, tumor location in lower/mid poles, maximum diameter > 10 mm, rich blood supply, heterogeneous enhancement, and elevated thyroid-stimulating hormone were independent CLNM risk factors. Receiver operating characteristic curves demonstrated the CatBoost model achieved optimal performance (training area under the curve: 0.791; test area under the curve: 0.804). SHapley Additive exPlanations analysis revealed maximum diameter > 10 mm, tumor location in lower/mid poles, and irregular margins were the top 3 contributing features. Tumor size > 10 mm is the most important predictor of CLNM. The CatBoost model demonstrated superior performance and, combined with SHapley Additive exPlanations analysis, provides a clinically applicable tool for personalized surgical planning by identifying high-risk patients who may benefit from prophylactic central lymph node dissection.

Amid rising diabetes prevalence, the burden of diabetes-related chronic kidney disease (DKD) is escalating, particularly in China. This study aimed to describe the epidemiological trends of DKD in China and develop a Bayesian age–period–cohort (BAPC) model to predict its incidence from 2022 to 2036. Data from the Global Burden of Disease Study 2021 (GBD 2021) were used to assess DKD’s burden in China between 1990 and 2021, including age-standardized incidence (ASIR), prevalence (ASPR), mortality (ASMR), and disability-adjusted life years (DALYs). BAPC models were applied to evaluate age, period, and cohort effects and to project future trends. Between 1990 and 2021, the ASIR for chronic kidney disease due to diabetes mellitus type 1 (DKD1) decreased (annual percent change (APC) = -0.57, P < 0.001), while ASPR increased (APC = 1.25, P < 0.001). ASMR and DALYs both declined (APC = -1.98, P < 0.001; APC = -1.99, P < 0.001). For chronic kidney disease due to diabetes mellitus type 2 (DKD2), ASIR slightly increased (APC = 0.44, P < 0.001), but both ASPR and ASMR decreased (APC = -0.23, P = 0.004; APC = -0.56, P < 0.001). Projections suggest an increase in DKD2 incidence and declining incidence for DKD1 by 2036. This study provides insights into the trends of DKD burden in China over the past three decades. The BAPC offers a reliable forecast for the next 15 years, aiding future research to reduce DKD’s impact in China.

Objective: To explore the clinical utility of ultrasound in evaluating and grading neuromuscular diseases in the lower extremities of patients with diabetes mellitus. Methods: A total of 126 inpatients from the Department of Diabetes at Zhangzhou Affiliated Hospital of Fujian Medical University, China, were recruited from June 2020 to December 2022. The cohort included 69 patients with type 2 diabetes mellitus (T2DM) and diabetic peripheral neuropathy (DPN group) and 57 patients with T2DM but without DPN (non-DPN group). Additionally, 80 healthy controls were included. High-frequency ultrasound was used to scan the common peroneal, sural, and tibial nerves, measuring their transverse (D1) and anteroposterior (D2) diameters, and calculating the cross-sectional area (CSA). Changes in the internal echo of the extensor digitorum brevis (EDB) muscle, including maximum thickness and CSA, were also recorded. The DPN group was further subdivided based on disease duration to assess ultrasonic changes over time and the statistical significance of these variations. Results: Ultrasonic changes such as uneven internal echo reduction, ill-defined epineurial boundaries, and obscured cribriform structures were most prevalent in the DPN group. Significant differences in ultrasound parameters (D1, D2, CSA) were observed among the groups (all P<0.05), with the most pronounced changes in the DPN group. In patients with a disease duration of over 15 years, a significant increase in CSA of lower extremity nerves and a decrease in CSA of the EDB were noted compared to those in the 5-10 years subgroup (19.89 +/- 0.98 vs 19.00 +/- 0.94; 5.25 +/- 0.74 vs 5.93 +/- 0.94; all P<0.05). Conclusions: High-frequency ultrasound provides a valuable imaging basis for diagnosing and monitoring DPN, demonstrating significant changes in nerve and muscle parameters among diabetic patients.

To investigate the ability of an auxiliary diagnostic model based on the YOLO-v7-based model in the classification of cervical lymphadenopathy images and compare its performance against qualitative visual evaluation by experienced radiologists. Three types of lymph nodes were sampled randomly but not uniformly. The dataset was randomly divided into for training, validation, and testing. The model was constructed with PyTorch. It was trained and weighting parameters were tuned on the validation set. Diagnostic performance was compared with that of the radiologists on the testing set. The mAP of the model was 96.4% at the 50% intersection-over-union threshold. The accuracy values of it were 0.962 for benign lymph nodes, 0.982 for lymphomas, and 0.960 for metastatic lymph nodes. The precision values of it were 0.928 for benign lymph nodes, 0.975 for lymphomas, and 0.927 for metastatic lymph nodes. The accuracy values of radiologists were 0.659 for benign lymph nodes, 0.836 for lymphomas, and 0.580 for metastatic lymph nodes. The precision values of radiologists were 0.478 for benign lymph nodes, 0.329 for lymphomas, and 0.596 for metastatic lymph nodes. The model effectively classifies lymphadenopathies from ultrasound images and outperforms qualitative visual evaluation by experienced radiologists in differential diagnosis.

Background: Differentiated thyroid cancer (DTC) progresses slowly, but patients with lung metastasis (LM) have a poor prognosis. The aim of this study was to develop and evaluate the predictive ability of machine learning (ML) models in estimating the risk of LM in patients with DTC and to identify the independent risk factors specific to different age and gender subgroups. Methods: The demographic and clinicopathological data of patients with DTC were obtained from two databases: firstly, the National Institutes of Health Surveillance, Epidemiology, and End Results (SEER) database [2010-2015], which provides extensive epidemiological and clinical information on cancer patients; secondly, the Zhangzhou Municipal Hospital Affiliated to Fujian Medical University [2014-2017], which focuses more on patients' specific clinicopathological characteristics and treatment outcomes. Common variables from both databases were extracted. The data were then split into training, testing and validation sets. The training set was used to build and train ML models, while the testing and validation set were employed to assess the performance of these models. In terms of model development, we established five boosting (XGBoost), and gradient boosting machine (GBM). For model validation, we utilized various operating characteristic (ROC) curve (AUROC), area under the precision-recall (PR) curve (PR-AUC), calibration curve, and decision curve analysis (DCA). The importance of various features was ranked and varied across the different age subgroups. In the female population, tumor size was an independent risk factor for LM, while it was not in the male population. GBM achieved an AUROC of 0.982, a Brier score of 0.047, an accuracy of 0.818, and an F1 score of 0.818 in the validation set, outperforming the other models. Conclusions: The GBM model emerged as an effective tool for identifying high-risk LM populations in DTC, with the potential to guide clinical practice and facilitate the development of individualized treatment plans. Further research to validate these findings across more diverse patient populations and clinical settings is recommended.

ABSTRACT:The objective of this study is to develop and validate the performance of 2 ultrasound (US) feature-guided machine learning models in distinguishing cervical lymphadenopathy. We enrolled 705 patients whose US characteristics of lymph nodes were collected at our hospital. B-mode US and color Doppler US features of cervical lymph nodes in both cohorts were analyzed by 2 radiologists. The decision tree and back propagation (BP) neural network were developed by combining clinical data (age, sex, and history of tumor) and US features. The performance of the 2 models was evaluated by calculating the area under the receiver operating characteristics curve (AUC), accuracy value, precision value, recall value, and balanced F score (F1 score). The AUC of the decision tree and BP model in the modeling cohort were 0.796 (0.757, 0.835) and 0.854 (0.756, 0.952), respectively. The AUC, accuracy value, precision value, recall value, and F1 score of the decision tree in the validation cohort were all higher than those of the BP model: 0.817 (0.786, 0.848) vs 0.674 (0.601, 0.747), 0.774 (0.737, 0.811) vs 0.702 (0.629, 0.775), 0.786 (0.739, 0.833) vs 0.644 (0.568, 0.720), 0.733 (0.694, 0.772) vs 0.630 (0.542, 0.718), and 0.750 (0.705, 0.795) vs 0.627 (0.541, 0.713), respectively. The US feature-guided decision tree model was more efficient in the diagnosis of cervical lymphadenopathy than the BP model.

OBJECTIVE Compared thyroid volumes measured by 2-D and 3-D US with those of resected specimens and proposed new models to improve measurement accuracy. METHODS This study included 80 patients who underwent total thyroidectomy. One 2D_model and one 3D_model were developed using piecewise linear regression analysis. The accuracy of these models was compared using an ellipsoid model (2-D_US value × 0.5), 3-D_US value, and Ying's model [1.76 + (2-D_US value × 0.38)]. RESULTS The new 2D_model was: V=2.66 + (0.71 * X1) - (1.51 * X2). In this model, if 2-D_US value <= 228.39, X1 = 2-D_US value and X2 = 0; otherwise, X1 = 2-D_US value and X2 = 2-D_US value - 228.39. The 3D_model was: V= 2.90 + (1.08 * X1) + (2.43 * X2). In this model, if 3-D_US value <= 102.06, X1 = 3-D_US value and X2 = 0; otherwise, X1 = 3-D_US value and X2 = 3-D_US value - 102.06. The accuracy of the new models was higher than that of the 3-D_US value, the ellipsoid model, and Ying's model (P<0.05). CONCLUSION The models established are more accurate than the traditional ones and can accurately measure thyroid volume.

目的 探讨早孕期全胎盘血管指数预测高风险孕妇子痫前期(PE)发生的价值.方法 取PE低风险孕妇与高风险孕妇各200例,早孕期采用三维能量多普勒超声(3D-PD)进行胎盘全容积扫查,用能量直方图计算胎盘体积及胎盘血管指数:血管形成指数(VI)、血流指数(FI)和血管形成-血流指数(VFI),比较高风险组与低风险组胎盘体积、胎盘血管指数的差异,分析高风险组孕妇早孕期胎盘血管指数预测PE的能力及其预测的临界值.结果 低风险组与高风险组的胎盘体积差异无统计学意义(P＞0.05),高风险组早孕期全胎盘血管指数明显低于低风险组,差异有统计学意义(P＜0.05).200例高风险孕妇44例发生PE,其血管指数VI、FI、VFI预测PE的ROC曲线下面积分别为0.821、0.741、0.865,VI、VFI与PE的发生有较好的相关性,特别是VFI;以VFI= 5.21为界值,预测PE的灵敏度、特异度、阳性预测值、阴性预测值、准确度为81.82％、79.48％、52.94％、93.94％、80.00％.结论 早孕期全胎盘血管指数能提高预测高风险孕妇PE发生的能力,可成为建立早孕期预测PE模型的新参数.

ObjectiveTo construct risk prediction models for cervical lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) under different thyroid disease backgrounds and to analyze and compare risk factors among different groups.MethodsThis retrospective study included 518 patients with PTC that was pathologically confirmed post-operatively from January 2021 to November 2021. Demographic, ultrasound and pathological data were recorded. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CLNM in the whole patient cohort and in patients grouped according to diagnoses of Hashimoto’s thyroiditis (HT), nodular goiter (NG), and no background disease. Prediction models were constructed for each group, and their performances were compared.ResultsAnalysis of the whole PTC patient cohort identified NG as independently associated with CLNM. The independent risk factors for patients with no background disease were the maximum thyroid nodule diameter and American College of Radiology Thyroid Imaging Reporting & Data System score; those for patients with HT were the maximum thyroid nodule diameter, ACR TI-RADS score, and multifocality; and those for patients with NG were the maximum thyroid nodule diameter, ACR TI-RADS score, multifocality and gender.ConclusionBackground thyroid disease impacts CLNM in PTC patients, and risk factors for CLNM vary among PTC patients with different background diseases. Ultrasound is useful for diagnosing background thyroid disease, which can inform treatment planning. Different prediction models are recommended for PTC cases with different thyroid diseases.

目的 探讨早孕期子宫动脉搏动指数(PI)联合全胎盘血管形成-血流指数(VFI)预测高风险孕妇发生子痫前期(PE)的价值.方法 取PE高风险孕妇200例(孕龄11-13+6周),经腹部测量孕妇双侧子宫动脉PI,采用三维能量多普勒超声(3D-PD)进行胎盘全容积扫查,用能量直方图计算全胎盘VFI.随访结果:44例发生PE的孕妇归入试验组,156例未发生PE的孕妇归入对照组.比较试验组与对照组早孕期子宫动脉PI及全胎盘VFI的差异,分析子宫动脉PI、全胎盘VFI预测PE的能力及其预测的临界值,比较子宫动脉PI、

The aim of this study was to develop a prognostic model for radioactive iodine (RAI) therapy outcome in patients with Graves' disease. We enrolled 127 patients. Information on RAI therapy, ultrasound indexes of thyroid, and other lifestyle factors was collected. The competing risk model was used to estimate the multivariable-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for nonhealing or recurrence of hyperthyroidism (NHRH). The performance of the model was assessed by receiver operator characteristic analysis and the Brier score and internally validated by bootstrap resampling. Then, a nomogram was developed. Forty-one cases (32.2%) of NHRH were documented. Positive Ki-67 expression, a higher dose of per-unit thyroid volume, and females showed lower risks of NHRH (all P < 0.05). The HR values (95% CI) were 0.42 (0.23, 0.79), 0.01 (0.00, 0.02), and 0.47 (0.25, 0.89), respectively. The bootstrap validation showed that the model had the highest accuracy and good calibration for predicting cumulative risk of NHRH at 180 days after RAI therapy (AUC = 0.772; 95% CI: 0.640-0.889, Brier score = 0.153). By decision curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.20 and 0.40. Ki-67, ultrasound volumetry, and scintigraphy techniques can play important roles in evaluating RAI therapy outcome in Graves' disease patients. The prediction nomogram shows reasonable accuracy in predicting NHRH.

To establish and validate a nomogram for predicting lymph node metastasis (LNM) of papillary thyroid carcinoma (PTC) in the cervical central region. This retrospective study included 287 PTC patients with 309 nodules treated from December 2018 to May 2020 at our hospital. The cohort was divided randomly into a training set and a testing set according to a 7:3 ratio. The training set contained 216 nodules, and the testing set contained 93 nodules. The nomogram was developed using the training set, and the data of the testing set were used to validate the performance of nomogram. The predictive accuracy and discriminative ability of the nomogram were determined by concordance index (C-index) and calibration curve. The study showed multifocality, thyroid lesion size, and American College of Radiology Thyroid Imaging, Reporting and Data System (TI-RADS) score were significantly independently associated with LNM in the cervical central region. In the testing set, the calibration curve showed that the nomogram had good discrimination with a C-index of 0.775 (95% confidence interval, 0.680-0.869) and adequate calibration (P = 0.808). By decision curve analysis and clinical impact curve analysis, the nomogram was shown to have a satisfactory net benefit between thresholds of 0.40 and 0.75. The nomogram can be used for predicting LNM of PTC in the cervical central region and may provide valuable guidance for planning the surgical treatment of PTC patients.

Previous studies suggest that triple-negative breast cancer (TNBC) may have unique imaging characteristics, however, studies focused on the imaging characteristics of TNBC are still limited. The aim of the present study is to analyze the ultrasonic characteristics of TNBC and to provide more reliable information on imaging diagnosis of TNBC. This retrospective study was performed including 162 TNBC patients with 184 TNBC lesions. 174 non-TNBC cases with 196 lesions were used as the control group. The median size of TNBC lesions and non-TNBC lesions were 23 mm × 16 mm and 21 mm × 15 mm, respectively. The shape of most breast cancer lesions was irregular. However, 15.30% (28/183) TNBC lesions and 16.84% (33/196) non-TNBC lesions were oval-shaped. Most breast cancer lesions (79.78% TNBC & 85.71% non-TNBC) were ill-defined. In comparison to non-TNBC, the distinctive ultrasonic characteristics of TNBC were summarized as three features: calcifications, posterior acoustic, and blood flow. Microcalcifications was less common in non-TNBC. The remarkable posterior acoustic characteristics on TNBC were no posterior acoustic features (136, 73.91%). Avascular pattern (21.74%) was also more common in TNBC. The other feature of TNBC was markedly hypoechoic lesions (23.91%). The above-mentioned differences between TNBC and non-TNBC were significant. 93.48% TBNC and 94.39% non–TNBC lesions were in BI-RADS-US category of 4A-5. The results indicate that TNBC has some distinctive ultrasound characteristics. Ultrasound is a useful adjunct in early detection of breast cancer. A combination of ultrasound with mammography is excellent for detecting breast cancer.

Background: Recently, circulating microRNAs (miRNAs) from maternal blood and amniotic fluid have been used as biomarkers for ventricular septal defect (VSD) diagnosis. However, whether circulating miRNAs are associated with fetal myocardium remains unknown. Methods: Dimethadione (DMO) induced a VSD rat model. The miRNA expression profiles of the myocardium, amniotic fluid and maternal serum were analyzed. Differentially expressed microRNAs (DE-microRNAs) were verified by qRT-PCR. The target gene of miR-1-3p was confirmed by dual luciferase reporter assays. Expression of amniotic fluid-derived DE-microRNAs was verified in clinical samples. Results: MiRNAs were differentially expressed in VSD fetal rats and might be involved in cardiomyocyte differentiation and apoptosis. MiR-1-3p, miR-1b and miR-293-5p were downregulated in the myocardium and upregulated in amniotic fluid/maternal serum. The expression of amniotic fluid-derived DE-microRNAs (miR-1-3p, miR-206 and miR-184) was verified in clinical samples. Dual luciferase reporter assays confirmed that miR-1-3p directly targeted SLC8A1/NCX1. Conclusion: MiR-1-3p, miR-1b and miR-293-5p are downregulated in VSD myocardium and upregulated in circulation and may be released into circulation by cardiomyocytes. MiR-1-3p targets SLC8A1/NCX1 and participates in myocardial apoptosis. MiR-1-3p upregulation in circulation is a direct and powerful indicator of fetal VSD and is expected to serve as a prenatal VSD diagnostic marker.