The accurate segmentation of breast ultrasound images is an important precondition for the lesion determination. The existing segmentation approaches embrace massive parameters, sluggish inference speed, and huge memory consumption. To tackle this problem, we propose T ²KD Attention U-Net (dual-Teacher Knowledge Distillation Attention U-Net), a lightweight semantic segmentation method combined double-path joint distillation in breast ultrasound images. Primarily, we designed two teacher models to learn the fine-grained features from each class of images according to different feature representation and semantic information of benign and malignant breast lesions. Then we leveraged the joint distillation to train a lightweight student model. Finally, we constructed a novel weight balance loss to focus on the semantic feature of small objection, solving the unbalance problem of tumor and background. Specifically, the extensive experiments conducted on Dataset BUSI and Dataset B demonstrated that the T ²KD Attention U-Net outperformed various knowledge distillation counterparts. Concretely, the accuracy, recall, precision, Dice, and mIoU of proposed method were 95.26%, 86.23%, 85.09%, 83.59%and 77.78% on Dataset BUSI, respectively. And these performance indexes were 97.95%, 92.80%, 88.33%, 88.40% and 82.42% on Dataset B, respectively. Compared with other models, the performance of this model was significantly improved. Meanwhile, compared with the teacher model, the number, size, and complexity of student model were significantly reduced (2.2×10 ⁶ vs. 106.1×10 ⁶, 8.4 MB vs. 414 MB, 16.59 GFLOPs vs. 205.98 GFLOPs, respectively). Indeedy, the proposed model guarantees the performances while greatly decreasing the amount of computation, which provides a new method for the deployment of clinical medical scenarios.
Humans ; Breast Neoplasms/diagnostic imaging* ; Female ; Ultrasonography, Mammary/methods* ; Image Processing, Computer-Assisted/methods* ; Algorithms ; Neural Networks, Computer ; Breast/diagnostic imaging*

Objective To investigate the value of ultrasonographic features combined with immunohistochemistry in predicting axillary lymph node metastasis in middle-aged women with breast cancer.Methods A retrospective analysis was conducted on 827 middle-aged female breast cancer patients who underwent surgical treatment at the Affiliated Hospital of Chengde Medical University from June 2017 to June 2023.Ultrasonographic and immunohistochemical information was collected,and the patients were randomly allocated into a training set(579 patients)and a validation set(248 patients).Univariate and multivariate Logistic regression analyses were performed to identify ultrasonographic and immunohistochemical risk factors associated with axillary lymph node metastasis in these patients,and a nomogram model was developed.Receiver operating characteristic curves and calibration curves were established to evaluate the performance of the nomogram model,and clinical decision curves were built to assess the clinical value of the model.Results The maximum diameter,morphology,boundary,calcification,and expression of human epidermal growth facor receptor 2 and Ki-67 in breast cancer lesions were identified as risk factors for predicting axillary lymph node metastasis in middle-aged women.The areas under the curve of the nomogram model on the training and validation sets were 0.747(0.707-0.787)and 0.714(0.647-0.780),respectively.Calibration curves and clinical decision curves indicated good consistency and performance of the model.Conclusion The nomogram model constructed based on ultrasonographic features and immunohistochemistry of the primary breast cancer lesion demonstrates high value in predicting axillary lymph node metastasis in middle-aged women with breast cancer.
Humans ; Female ; Breast Neoplasms/diagnostic imaging* ; Middle Aged ; Lymphatic Metastasis/diagnostic imaging* ; Axilla ; Retrospective Studies ; Nomograms ; Ultrasonography ; Immunohistochemistry ; Lymph Nodes/diagnostic imaging* ; Risk Factors ; Ki-67 Antigen

Sentinel lymph nodes (SLNs) are the first station of lymph nodes that extend from the breast tumor to the axillary lymphatic drainage. The pathological status of these LNs can predict that of the entire axillary lymph node. Therefore, the accurate identification of SLNs is necessary for sentinel lymph node biopsy (SLNB) to replace axillary lymph node dissection (ALND). The quality of life and prognosis of breast cancer patients are related to proper surgical treatment after the precise identification of SLNs. Some of the SLN tracers that have been identified include radioisotope, nano-carbon, indocyanine green (ICG), and methylene blue (MB). However, these tracers have certain limitations, such as pigmentation, radiation dangers, and the requirement for costly detection equipment. Ultrasound contrast agents (UCAs) have good specificity and sensitivity, and thus can compensate for some shortcomings of the mentioned tracers. This technique is also being applied to SLNB in patients with breast cancer, and can even provide an initial judgment on SLN status. Contrast-enhanced ultrasound (CEUS) has the advantages of high distinguishability, simple operation, no radiation harm, low cost, and accurate localization; therefore, it is expected to replace the traditional biopsy methods. In addition, it can significantly enhance the accuracy of SLN localization and shorten the operation time.
Humans ; Female ; Sentinel Lymph Node/pathology* ; Breast Neoplasms/pathology* ; Quality of Life ; Sentinel Lymph Node Biopsy/methods* ; Ultrasonography/methods* ; Lymph Nodes/surgery*

Early screening is an important means to reduce breast cancer mortality. In order to solve the problem of low breast cancer screening rates caused by limited medical resources in remote and impoverished areas, this paper designs a breast cancer screening system aided with portable ultrasound Clarius. The system automatically segments the tumor area of the B-ultrasound image on the mobile terminal and uses the ultrasound radio frequency data on the cloud server to automatically classify the benign and malignant tumors. Experimental results in this study show that the accuracy of breast tumor segmentation reaches 98%, and the accuracy of benign and malignant classification reaches 82%, and the system is accurate and reliable. The system is easy to set up and operate, which is convenient for patients in remote and poor areas to carry out early breast cancer screening. It is beneficial to objectively diagnose disease, and it is the first time for the domestic breast cancer auxiliary screening system on the mobile terminal.
Breast/pathology* ; Breast Neoplasms/pathology* ; Diagnosis, Computer-Assisted ; Early Detection of Cancer ; Female ; Humans ; Ultrasonography ; Ultrasonography, Mammary/methods*

Breast cancer has now become the leading cancer in women. The development of breast ultrasound artificial intelligence (AI) diagnostic technology is conducive to promoting the precise diagnosis and treatment of breast cancer and alleviating the heavy medical burden due to the unbalanced regional development in China. In recent years, on the basis of improving diagnostic efficiency, AI technology has been continuously combined with various clinical application scenarios, thereby providing more comprehensive and reliable evidence-based suggestions for clinical decision-making. Although AI diagnostic technologies based on conventional breast ultrasound gray-scale images and cutting-edge technologies such as three-dimensional (3D) imaging and elastography have been developed to some extent, there are still technical pain points, diffusion difficulties and ethical dilemmas in the development of AI diagnostic technologies for breast ultrasound.
Artificial Intelligence ; Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Elasticity Imaging Techniques/methods* ; Female ; Humans ; Ultrasonography, Mammary/methods*

OBJECTIVE: To evaluate the value of ultrasound S-Detect in the diagnosis of breast masses. METHODS: A total of 85 breast masses in 62 female patients were diagnosed by S-Detect technique and conventional ultrasound. The diagnostic efficacy of conventional ultrasound and S-Detect technique was analyzed and compared with postoperative pathological results as the gold standard. RESULTS: When operated by junior physicians, the diagnostic efficacy of conventional ultrasound was significantly lower than that of S-Detect technique (P < 0.05), but this difference was not observed in moderately experienced and senior physicians (P>0.05). S-Detect technique was positively correlated with the diagnostic results of senior physicians (r=0.97). Using S-Detect technique, the diagnostic efficacy did not differ significantly between the long axis section and its vertical section (P>0.05). Routine ultrasound showed a better diagnostic efficacy than S-Detect for breast masses with a diameter below 20 mm (P < 0.05), but for larger breast masses, its diagnostic efficacy was significantly lower than that of SDetect (P < 0.05). CONCLUSION S-Detect can be used in differential diagnosis of benign and malignant breast masses, and its diagnostic efficiency can be comparable with that of BI-RADS classification for moderately experienced and senior physicians, but its diagnostic efficacy can be low for breast masses less than 20 mm in diameter.
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Female ; Humans ; Sensitivity and Specificity ; Ultrasonics ; Ultrasonography ; Ultrasonography, Mammary/methods*

Objective To explore the value of elastography strain ratio(SR)combined with breast ultrasound imaging reporting and data system(BI-RADS-US)in the differential diagnosis of breast nodules.Methods A total of 471 breast nodules(from 471 patients)were reclassified by SR combined with BI-RADS-US.With the pathology results as gold standard,the area under the receiver operating characteristic(ROC)curve(AUC)was employed to evaluate the diagnostic performance,and the sensitivity,specificity,and accuracy were compared between the combined method and BI-RADS-US.Results Among the 471 breast nodules,180 nodules were benign and 291 were malignant.The AUC of the combined method was statistically significantly higher than that of BI-RADS-US(0.798 vs. 0.730;Z= 2.583, P= 0.010).SR,BI-RADS-US,and the combined method for diagnosing breast nodules had the sensitivity of 86.6%,99.0%,and 96.6%,the specificity of 67.2%,47.2%,and 63.3%,and the accuracy of 79.2%,79.2%,and 83.9%,respectively.The combined method increased the specificity from 47.2%(BI-RADS-US)to 63.3%(χ
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Elasticity Imaging Techniques ; Female ; Humans ; ROC Curve ; Sensitivity and Specificity ; Ultrasonography, Mammary

Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.
Algorithms ; Breast Neoplasms/diagnostic imaging* ; Computers ; Diagnosis, Computer-Assisted ; Humans ; Support Vector Machine ; Ultrasonography

Triple-negative breast cancer is a complex type of breast cancer,the most common malignant tumor in women.Since the early image features of triple-negative breast cancer appear benign tumor with rapid growth,this cancer has progressed into the middle and late stages once diagnosed,which leads to high mortality.Therefore,the diagnosis of triple-negative breast cancer has always been a clinical difficulty.This article summarizes the role of ultrasound in the diagnosis and treatment of triple-negative breast cancer.The extracted multi-mode ultrasound features will facilitate the early detection of this cancer and improve the prognosis of these patients.
Breast Neoplasms/diagnostic imaging* ; Diagnosis, Differential ; Female ; Humans ; Triple Negative Breast Neoplasms/diagnostic imaging* ; Ultrasonography ; Ultrasonography, Mammary

breast lesions and in predicting the biological characteristics of invasive breast cancer.MATERIALS AND METHODS: Between January 2016 and February 2019, this study included 368 women with 368 pathologically proven breast lesions, which appeared as poor-quality regions in the QM of SWE. To measure shear-wave velocity (SWV), seven regions of interest were placed in each lesion with and without QM guidance. Under QM guidance, poor-quality areas were avoided. Diagnostic performance was calculated for mean SWV (SWV(mean)), max SWV (SWV(max)), and standard deviation (SD) with QM guidance (SWV(mean) + QM, SWV(max) + QM, and SD + QM, respectively) and without QM guidance (SWV(mean) − QM, SWV(max) − QM, and SD − QM, respectively). For invasive cancers, the relationship between SWV findings and biological characteristics was investigated with and without QM guidance.RESULTS: Of the 368 women (mean age, 47 years; SD, 10.8 years) enrolled, 159 had benign breast lesions and 209 had malignant breast lesions. SWV(mean) + QM (3.6 ± 1.39 m/s) and SD + QM (1.02 ± 0.84) were significantly different from SWV(mean) − QM (3.29 ± 1.22 m/s) and SD − QM (1.46 ± 1.06), respectively (all p < 0.001). For differential diagnosis of breast lesions, the sensitivity and areas under the receiver operating characteristic curve (AUC) of SWV(mean) + QM (sensitivity: 89%; AUC: 0.932) were better than those of SWV(mean) − QM (sensitivity, 84.2%; AUC, 0.912) (all p < 0.05). There was no significant difference in sensitivity and specificity between SD + QM and SD − QM (all p = 1.000). Among the biological characteristics of invasive cancers, lymphovascular involvement, axillary lymph node metastasis, negative estrogen receptor status, negative progesterone receptor status, positive human epidermal growth factor receptor status, and aggressive molecular subtypes showed higher SWV(mean) + QM (all p < 0.05), while only lymphovascular involvement showed higher SWV(mean) − QM (p = 0.036).CONCLUSION: The use of QM in SWE might improve the diagnostic performance for breast lesions and facilitate prediction of the biological characteristics of invasive breast cancers.]]>
Area Under Curve ; Breast Neoplasms ; Breast ; Diagnosis ; Diagnosis, Differential ; Elasticity Imaging Techniques ; Estrogens ; Female ; Humans ; Lymph Nodes ; Neoplasm Metastasis ; Population Characteristics ; Receptor, Epidermal Growth Factor ; Receptors, Progesterone ; ROC Curve ; Sensitivity and Specificity ; Ultrasonography