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*

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*

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

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

diagnosis of 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

To obtain ultrasound and thermal tomography images of breast cancer during its growth and to assess the value of thermal tomography in detecting breast cancer. Breast cancer models were established with NOD/SCID mice and SD rats. These animal models were examined by thermal tomography,plain ultrasound,and contrast-enhanced ultrasound. Tumor tissues were stained with CD34 to explore the relationship between tumor heat production and vascular pathology. Thermal tomography detected breast cancer 2-4 days earlier than ultrasound. The expression of CD34 in tumor tissues was increased,along with thickened,increased,and irregular blood vessels. Thermal tomography can detect early breast cancer and is a promising tool for screening breast cancer.
Animals ; Breast Neoplasms ; diagnostic imaging ; Early Diagnosis ; Mice ; Mice, Inbred NOD ; Mice, SCID ; Neoplasms, Experimental ; diagnostic imaging ; Rats ; Rats, Sprague-Dawley ; Tomography ; Ultrasonography, Mammary

PURPOSE: Absorbable retaining thread (ART) needle localization utilizes a guiding needle with a thread; this technique was invented to reduce patient discomfort and wire migration. We investigated the feasibility of ultrasound (US)-guided ART needle localization for nonpalpable breast lesions. METHODS: ART needle localization was performed for 26 nonpalpable breast lesions in 26 patients who were scheduled to undergo surgical excision the day after localization. Seventeen breast lesions were initially diagnosed as invasive ductal carcinoma, six as ductal carcinomas in situ, and one as fibrocystic change. The other two cases without an initial pathologic diagnosis had suspicious US features, and excision was planned concomitantly with contralateral breast cancer surgery. The primary outcome was the technical success rate of ART needle localization confirmed by US immediately after the procedure, and the secondary outcomes were the percentage of clear margins on pathology and the complication rate of ART needle localization. RESULTS: The technical success rate of ART needle localization was 96.2% (25 of 26 patients), and the ART was located 1 cm away from the mass in one patient (3.8%). The lesions were successfully removed with clear margins in all 26 patients. No significant complications related to ART needle localization were observed. CONCLUSION: ART needle localization can be an alternative to wire needle localization for nonpalpable breast lesions.
Breast Neoplasms ; Breast ; Carcinoma, Ductal ; Diagnosis ; Humans ; Needles ; Pathology ; Surgery, Computer-Assisted ; Ultrasonography

PURPOSE: The aim of this study was to compare the performance of image analysis for predicting breast cancer using two distinct regression models and to evaluate the usefulness of incorporating clinical and demographic data (CDD) into the image analysis in order to improve the diagnosis of breast cancer. METHODS: This study included 139 solid masses from 139 patients who underwent a ultrasonography-guided core biopsy and had available CDD between June 2009 and April 2010. Three breast radiologists retrospectively reviewed 139 breast masses and described each lesion using the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We applied and compared two regression methods-stepwise logistic (SL) regression and logistic least absolute shrinkage and selection operator (LASSO) regression-in which the BI-RADS descriptors and CDD were used as covariates. We investigated the performances of these regression methods and the agreement of radiologists in terms of test misclassification error and the area under the curve (AUC) of the tests. RESULTS: Logistic LASSO regression was superior (P < 0.05) to SL regression, regardless of whether CDD was included in the covariates, in terms of test misclassification errors (0.234 vs. 0.253, without CDD; 0.196 vs. 0.258, with CDD) and AUC (0.785 vs. 0.759, without CDD; 0.873 vs. 0.735, with CDD). However, it was inferior (P < 0.05) to the agreement of three radiologists in terms of test misclassification errors (0.234 vs. 0.168, without CDD; 0.196 vs. 0.088, with CDD) and the AUC without CDD (0.785 vs. 0.844, P < 0.001), but was comparable to the AUC with CDD (0.873 vs. 0.880, P=0.141). CONCLUSION: Logistic LASSO regression based on BI-RADS descriptors and CDD showed better performance than SL in predicting the presence of breast cancer. The use of CDD as a supplement to the BI-RADS descriptors significantly improved the prediction of breast cancer using logistic LASSO regression.
Area Under Curve ; Biopsy ; Breast Neoplasms* ; Breast* ; Diagnosis* ; Humans ; Information Systems ; Logistic Models ; Retrospective Studies ; Subject Headings ; Ultrasonography*

PURPOSE: The purpose of this study was to evaluate the usefulness of applying computer-aided diagnosis (CAD) to breast ultrasound (US), depending on the reader's experience with breast imaging. METHODS: Between October 2015 and January 2016, two experienced readers obtained and analyzed the grayscale US images of 200 cases according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and categories. They additionally applied CAD (S-Detect) to analyze the lesions and made a diagnostic decision subjectively, based on grayscale US with CAD. For the same cases, two inexperienced readers analyzed the grayscale US images using the BI-RADS lexicon and categories, added CAD, and came to a subjective diagnostic conclusion. We then compared the diagnostic performance depending on the reader's experience with breast imaging. RESULTS: The sensitivity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 91.7%, 75.0%, 75.0%, and 66.7%, respectively. The specificity values for the experienced readers, inexperienced readers, and CAD (for experienced and inexperienced readers) were 76.6%, 71.8%, 78.2%, and 76.1%, respectively. When diagnoses were made subjectively in combination with CAD, the specificity significantly improved (76.6% to 80.3%) without a change in the sensitivity (91.7%) in the experienced readers. After subjective combination with CAD, both of the sensitivity and specificity improved in the inexperienced readers (75.0% to 83.3% and 71.8% to 77.1%). In addition, the area under the curve improved for both the experienced and inexperienced readers (0.84 to 0.86 and 0.73 to 0.80) after the addition of CAD. CONCLUSION: CAD is more useful for less experienced readers. Combining CAD with breast US led to improved specificity for both experienced and inexperienced readers.
Breast Neoplasms ; Breast* ; Diagnosis* ; Diagnosis, Computer-Assisted ; Information Systems ; Sensitivity and Specificity ; Ultrasonography*