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Background: This study aims to comprehensively evaluate the accuracy and effectiveness of ultrasound imaging based on artificial intelligence algorithms in predicting lymph node metastasis in breast cancer patients through a meta-analysis., Methods: We systematically searched PubMed, Embase, and Cochrane Library for literature published up to May 2023. The search terms included artificial intelligence, ultrasound, breast cancer, and lymph node. Studies meeting the inclusion criteria were selected, and data were extracted for analysis. The main evaluation indicators included sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and area under the curve (AUC). The heterogeneity was assessed using the Cochrane Q test combined with the I^2 statistic expressing the percentage of total effect variation that can be attributed to the effect variation between studies, as recommended by the Cochrane Handbook for heterogeneity quantification. A threshold p-value of 0.10 was considered to compensate for the low power of the Q test. Sensitivity analysis was performed to assess the stability of individual studies, and publication bias was determined with funnel plots. Additionally, fagan plots were used to assess clinical utility., Results: Ten studies involving 4726 breast cancer patients were included in the meta-analysis. The results showed that ultrasound imaging based on artificial intelligence algorithms had high accuracy and effectiveness in predicting lymph node metastasis in breast cancer patients. The pooled sensitivity was 0.88 (95% CI: 0.81-0.93; P < 0.001; I2 = 84.68), specificity was 0.75 (95% CI: 0.66-0.83; P < 0.001; I 2  = 91.11), and AUC was 0.89 (95% CI: 0.86-0.91). The positive likelihood ratio was 3.5 (95% CI: 2.6-4.8), negative likelihood ratio was 0.16 (95% CI: 0.10-0.26), and diagnostic odds ratio was 23 (95% CI: 13-40). However, the combined sensitivity of ultrasound imaging based on non-AI algorithms for predicting lymph node metastasis in breast cancer patients was 0.78 (95%CI: 0.63-0.88), the specificity was 0.76 (95%CI: 0.63-0.86), and the AUC was 0.84 (95%CI: 0.80-0.87). The positive likelihood ratio was 3.3 (95% CI: 1.9-5.6), the negative likelihood ratio was 0.29 (95% CI: 0.15-0.54), and the diagnostic odds ratio was 11 (95% CI: 4-33). Due to limited sample size (n = 2), meta-analysis was not conducted for the outcome of predicting lymph node metastasis burden., Conclusion: Ultrasound imaging based on artificial intelligence algorithms holds promise in predicting lymph node metastasis in breast cancer patients, demonstrating high accuracy and effectiveness. The application of this technology helps in the diagnosis and treatment decisions for breast cancer patients and is expected to become an important tool in future clinical practice., Competing Interests: Declaration of competing interest None of the authors have any personal, financial, commercial, or academic conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Pyrotinib is a novel EGFR/HER2 dual tyrosine kinase inhibitor developed in China, while its role in neoadjuvant therapy of HER2-positive (HER2 + ) breast cancer lacks evidence. The current study aimed to explore the efficacy and safety of neoadjuvant pyrotinib plus docetaxel/liposomal doxorubicin/cyclophosphamide (TAC) for HER2 + breast cancer., Methods: A total of 27 HER2 + breast cancer patients received neoadjuvant pyrotinib plus TAC for 6 cycles, then surgery was performed. The clinical and pathological responses, and adverse events were evaluated., Results: Complete response rate, objective response rate, and disease control rate were 0.0%, 44.4% and 100.0% after 2 treatment cycles; 0.0%, 37.0%, and 100.0% after 4 treatment cycles; 37.0%, 37.0%, and 96.3% after 6 treatment cycles; as well as 37.0%, 44.4%, and 100.0% based on the best clinical response. Regarding pathological response, there were 1 (2.7%), 3 (11.1%), 8 (29.6%), 5 (18.5%), and 10 (37.0%) patients realizing Miller-Payne grade (G) 1, G2, G3, G4, and G5, respectively; besides, 10 (37.0%) patients achieved total pathological complete response (pCR), 10 (37.0%) patients realized pCR in breast, and 23 (85.2%) patients achieved pCR in lymph node. Additionally, adverse events included diarrhea (81.5%), dental ulcer (7.4%), and hand-foot syndrome (3.7%); meanwhile, grade 3-4 adverse event consisted of only diarrhea (11.1%)., Conclusion: Neoadjuvant pyrotinib plus TAC treatment is efficient and safe in HER2 + breast cancer patients, while further validation is needed., (© 2022. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.)

Background: MicroRNAs (miRNAs) are involved in the progression of diverse human cancers. This work aimed to delve into how microRNA-135a-5p (miR-135a-5p) affects the biological behaviors of Breast Cancer (BC) cells., Methods: Gene Expression Omnibus (GEO) datasets were used to analyze the expression differences of miR-135a-5p in cancer tissues of BC patients. Quantitative real-time PCR and western blot were conducted to detect miR-135a-5p and Bcl-2 Associated Athanogene (BAG3) expression levels in BC tissues and cells, respectively. The proliferation, migration, invasion, and cell cycle of BC cells were detected by cell counting kit-8 assay, BrdU assay, wound healing assay, transwell assay, and flow cytometry. The targeted relationship between miR-135a-5p and BAG3 mRNA 3'UTR predicted by bioinformatics was further testified by a dual-luciferase reporter gene assay. Pearson's correlation analysis was adopted to analyze the correlation between miR-135a-5p expression and BAG3 expression. The downstream pathways of BAG3 were analyzed by the LinkedOmics database., Results: MiR-135a-5p was significantly down-regulated and BAG3 expression was significantly raised in BC tissues. MiR-135a-5p overexpression repressed the viability, migration and invasion of BC cells, and blocked cell cycle progression in G0/G1 phase while inhibiting miR-135a-5p worked oppositely. BAG3 was verified as a target of miR-135a-5p. Overexpression of BAG3 reversed the impacts of miR-135a-5p on the malignant biological behaviors of BC cells. The high expression of BAG3 was associated with the activation of the cell cycle, mTOR and TGF-β signaling pathways., Conclusion: MiR-135a-5p regulates BAG3 to repress the growth, migration, invasion, and cell cycle progression of BC cells., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2022. Published by Elsevier España, S.L.U.)

Breast cancer (BC) ranks first for incidence and mortality in gynecological malignant tumors. This study aims to investigate the diagnostic value of Tank-binding kinase 1 (TBK1) and its correlation with androgen receptor (AR) and other serum cancer-related biomarkers in BC patient. The present observational study included 451 female BC patients and 451 healthy controls. Serum levels of TBK1, AR and other cancer-related biomarkers were detected in all the patients and healthy controls. Patients' demographic data and clinical data including age, body mass index (BMI), tumor node Metastasis (TNM), pathological type, tumor size and lymph node metastasis were collected. The follow-up lasted for 5 years. The deceased group had higher rate of patients with TNM III~IV, lymph node metastasis or tumor diameter >2. Deceased group had much higher rate of patients with negative ER and positive Ki67. Besides, increased TBK1 was found in BC patients with positive correlation with AR, CA15-3, CA125, CEA, and CA19-9. Serum TBK1 was associated with the clinic outcomes of BC patients and those with high TBK1 had lower 5-year survival rate. Moreover, cutoff value of 13.95 ng/mL TBK1 showed AUC of 0.981 (93.6% for sensitivity and 86.3% for specificity) for diagnosing BC, and cutoff value of 22.65 ng/mL TBK1 had AUC of 0.996 (97.7% for sensitivity and 96.3% for specificity) for diagnosing the death of BC patients. Serum TBK1 was positively correlated with AR and other serum cancer-related biomarkers. In addition, high TBK1 predicted the poor prognosis and might be used for the diagnosis of BC., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

This study was to analyze the effect of the combined application of deep learning technology and ultrasound imaging on the effect of breast-conserving surgery for breast cancer. A deep label distribution learning (LDL) model was designed, and the semiautomatic segmentation algorithm based on the region growing and active contour technology (RA) and the segmentation model based on optimized nearest neighbors (ON) were introduced for comparison. The designed algorithm was applied to the breast-conserving surgery of breast cancer patients. According to the difference in intraoperative guidance methods, 102 female patients with early breast cancer were divided into three groups: 34 cases in W1 group (ultrasound guidance based on deep learning segmentation model), 34 cases in W2 group (ultrasound guidance), and 34 cases in W3 group (palpation guidance). The results revealed that the tumor area segmented by the LDL algorithm constructed in this study was closer to the real tumor area; the segmentation accuracy (AC), Jaccard, and true-positive (TP) values of the LDL algorithm were obviously greater than those of the RA and ON algorithms, while the false-positive (FP) value was significantly lower in contrast to the RA and ON algorithms, showing statistically observable differences ( P  < 0.05); the actual resection volume of the patients in the W1 group was the closest to the ideal resection volume, which was much smaller in contrast to that of the patients in the W2 and W3 groups, showing statistical differences ( P  < 0.05); the positive margins of the patients in the W1 group were statistically lower than those in the W2 and W3 groups ( P  < 0.05). In addition, 1 patient in the W1 group was not satisfied with the cosmetic effect, 3 patients in the W2 group were not satisfied with the cosmetic effect, and 9 patients in the W3 group were not satisfied with the cosmetic effect. Finally, it was found that the ultrasound image based on the deep LDL model effectively improved the AC of tumor resection and negative margins, reduced the probability of normal tissue being removed, and improved the postoperative cosmetic effect of breast., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2021 Hongxu Zhang et al.)