BACKGROUND: In light of the significant clinical benefits of antibody-drug conjugates in clinical trials, the human epidermal growth factor receptor 2 (HER2)-low category in breast cancers has gained increasing attention. Therefore, we studied the clinicopathological characteristics of Chinese patients with hormone receptor (HR)-positive/HER2-low early-stage breast cancer and developed a recurrence risk prediction model. METHODS: Female patients with HR-positive/HER2-low early-stage breast cancer treated in 29 hospitals of the Chinese Society of Breast Surgery (CSBrS) from Jan 2015 to Dec 2016 were enrolled. Their clinicopathological data and prognostic information were collected, and machine learning methods were used to analyze the prognostic factors. RESULTS: In total, 25,096 patients were diagnosed with breast cancer in 29 hospitals of CSBrS from Jan 2015 to Dec 2016, and clinicopathological data for 6486 patients with HER2-low early-stage breast cancer were collected. Among them, 5629 patients (86.79%) were HR-positive. The median follow-up time was 57 months (4, 76 months); the 5-year disease-free survival (DFS) rate was 92.7%, and the 5-year overall survival (OS) rate was 97.7%. In total, 412 cases (7.31%) of metastasis were observed, and 124 (2.20%) patients died. Multivariate Cox regression analysis revealed that T stage, N stage, lymphovascular thrombosis, Ki-67 index, and prognostic stage were associated with recurrence and metastasis ( P <0.05). A recurrence risk prediction model was established using the random forest method and exhibited a sensitivity of 81.1%, specificity of 71.7%, positive predictive value of 74.1%, and negative predictive value of 79.2%. CONCLUSION: Most of patients with HER2-low early-stage breast cancer were HR-positive, and patients had favorable outcome; tumor N stage, lymphovascular thrombosis, Ki-67 index, and tumor prognostic stage were prognostic factors. The HR-positive/HER2-low early-stage breast cancer recurrence prediction model established based on the random forest method has a good reference value for predicting 5-year recurrence events. REGISTRITATION ChiCTR.org.cn, ChiCTR2100046766.
Humans ; Female ; Breast Neoplasms/diagnosis* ; Ki-67 Antigen ; Receptor, ErbB-2 ; Prognosis ; Thrombosis ; Receptors, Progesterone

Humans ; Female ; Diagnosis, Differential ; Breast/pathology* ; Carcinoma, Intraductal, Noninfiltrating/pathology* ; Breast Neoplasms/pathology*

The estimated new cases of breast cancer (BC) patients were 2.26 million in 2020, which accounted for 11.7% of all cancer patients, making it the most prevalent cancer worldwide. Early detection, diagnosis and treatment are crucial to reduce the mortality, and improve the prognosis of BC patients. Despite the widespread use of mammography screening as a tool for BC screening, the false positive, radiation, and overdiagnosis are still pressing issues that need to be addressed. Therefore, it is urgent to develop accessible, stable, and reliable biomarkers for non-invasive screening and diagnosis of BC. Recent studies indicated that the circulating tumor cell DNA (ctDNA), carcinoembryonic antigen (CEA), carbohydrate antigen 15-3 (CA15-3), extracellular vesicles (EV), circulating miRNAs and BRCA gene from blood, and the phospholipid, miRNAs, hypnone and hexadecane from urine, nipple aspirate fluid (NAF) and volatile organic compounds (VOCs) in exhaled gas were closely related to the early screening and diagnosis of BC. This review summarizes the advances of the above biomarkers in the early screening and diagnosis of BC.
Humans ; Female ; Biomarkers, Tumor ; Early Detection of Cancer ; Breast Neoplasms/diagnosis* ; Prognosis ; MicroRNAs/genetics*

OBJECTIVE: To investigate the value of quantitative synthetic magnetic resonance imaging (SyMRI) in distinguishing between benign and malignant breast lesions. METHODS: We retrospectively collected data of preoperative conventional MRI and multi-dynamic multi-echo sequences from 95 patients with breast lesions showing mass-type enhancement on DCE-MRI, including 27 patients with benign lesions and 68 with malignant lesions. The MRI features of the lesions (shape, margin, internal enhancement pattern, time-signal intensity curve, and T2WI signal) were analyzed, and for each lesion, SyMRI-generated quantitative parameters including T1 and T2 relaxation time and proton density (PD) were measured before and after enhancement and recorded as T1p, T2p, PDp and T1e, T2e, and PDe, respectively. The relative change rate of each parameter was calculated. Logistic regression and all-subset regression analyses were performed for variable selection to construct diagnostic models of the breast lesions, and receiver-operating characteristic (ROC) analysis was used to assess the performance of each model for differentiation of benign and malignant lesions. RESULTS: There were significant differences in the MRI features between benign and malignant lesions (P < 0.05). All the SyMRI-generated quantitative parameters, with the exception of T2e and Pdp, showed significant differences between benign and malignant lesions (P < 0.05). Among the constructed diagnostic models, the model based on all the DCE-MRI features combined with SyMRI parameters T2p and T1e (DCE-MRI+T2p+T1e) showed the best performance in the differential diagnosis malignant breast masses with an AUC of 0.995 (95% CI: 0.983-1.000). CONCLUSION Quantitative SyMRI can be used for differential diagnosis of benign and malignant breast lesions.
Breast/diagnostic imaging* ; Breast Neoplasms/diagnostic imaging* ; Contrast Media ; Diagnosis, Differential ; Female ; Humans ; Magnetic Resonance Imaging/methods* ; ROC Curve ; Retrospective Studies

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 develop a nomogram model for the differential diagnosis of benign and malignant breast BI-RADS (Breast Imaging Reporting and Data System) category 4 nodules based on serum tumor specific protein 70 (SP70) and conventional laboratory indicators and validate its predictive efficacy. Methods: A case-control study design was used to retrospectively analyze the data of 429 female patients diagnosed with BI-RADS category 4 breast nodules by breast color doppler flow imaging at the First Affiliated Hospital of Nanjing Medical University from January 2021 to April 2022 with an age range of 16 to 91 years and a median age of 50 years, and the patients were divided into a training cohort (314 patients) and a validation cohort (115 patients) according to the inclusion time successively. Using postoperative pathological findings as the"gold standard", univariate and multivariate logistic regression analyses were used to identify the predictor variables used for the model. The nomogram, receiver operating characteristic (ROC) curves and calibration curves were drawn for the prediction model, and the discrimination and calibration of the model were evaluated using the consistency index (C-index) and calibration plots. Results: The postoperative pathological results showed that 286 (66.7%) were malignant nodules and 143 (33.3%) were benign nodules of 429 breast BI-RADS category 4 nodules. The serum SP70 (OR=1.227,95%CI: 1.033-1.458,P=0.020), NLR (OR=1.545,95%CI: 1.047-2.280,P=0.028), LDL-C (OR=2.215, 95%CI: 1.354-3.622, P=0.002), GLU (OR=2.050,95%CI:1.222-3.438,P=0.007), PT (OR=1.383,95%CI: 1.046-1.828,P=0.023), nodule diameter (OR=1.042, 95%CI: 1.008-1.076, P=0.015) and age (OR=1.062,95%CI: 1.011-1.116,P=0.016) were independent risk factors which could be used to distinguish benign and malignant breast BI-RADS category 4 nodules (P<0.05). The nomogram was plotted by the above seven independent variables, and the concordance index (C-index) for the training cohort and validation cohort were 0.842 (95%CI:0.786-0.898) and 0.787 (95%CI:0.687-0.886), respectively. The sensitivity and specificity of using this model to identify benign and malignant breast BI-RADS category 4 nodules in the training and validation cohort were 83.5%, 72.5% and 79.2%, 73.6%, respectively. The calibration curves showed good agreement between the predicted and actual values in the nomogram. Conclusions: This study combined serum SP70, conventional laboratory indicators and breast color doppler flow imaging to develop a nomogram model for the differential diagnosis of benign and malignant breast BI-RADS category 4 nodules. The model may have good predictive efficacy and may provide a basis for clinical treatment options, which is beneficial for guiding breast cancer screening and prevention.
Female ; Humans ; Middle Aged ; Adolescent ; Young Adult ; Adult ; Aged ; Aged, 80 and over ; Diagnosis, Differential ; Retrospective Studies ; Case-Control Studies ; Breast/pathology* ; Breast Neoplasms/pathology*

Breast/pathology* ; Breast Neoplasms/pathology* ; Carcinoma in Situ/pathology* ; Carcinoma, Lobular/pathology* ; Diagnosis, Differential ; Female ; Humans ; Hyperplasia/pathology*

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