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简介：AbstractBackground:The current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.Methods:Taking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR-), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.Results:The accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87-0.91, 0.89-0.92, 0.87-0.91, and 0.86-0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).Conclusions:The CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registration:Chictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.

简介：AbstractBackground:Endoscopic ultrasound (EUS)-guided transmural drainage for pancreatic fluid collections (PFCs) has become the first-line treatment with quicker recovery and more minor injury compared with surgery and percutaneous drainage. The efficacy of stents implantation and drainage for different PFCs remains controversial, especially lumen-apposing metal stents (LAMS). This study aimed to compare the efficacy and safety of LAMS drainage for pancreatic pseudocysts (PPC) and walled-off necrosis (WON).Methods:A meta-analysis was performed for LAMS drainage for WON and PPC by systematically searching PubMed, Cochrane, and Embase databases from January 2010 to January 2020. From 2017 to 2019, 12 patients who were treated with LAMS drainage for PFCs in our medical center were also reviewed and included in this study.Results:Combining 11 copies of documents with the data from our medical center, a total of 585 patients with PFCs were enrolled in this meta-analysis, including 343 patients with WON and 242 with PPC. The technical success rate in WON is not significantly different from that of PPC (P = 0.08 > 0.05). The clinical success of LAMS placement was achieved in 99% vs 89% in PPC and WON, respectively (RR = 0.92, 95% CI: 0.86-0.98, P = 0.01 < 0.05). The further intervention of direct endoscopic necrosectomy was required by 60% of patients in WON group. There was no significant difference in the incidence of adverse events, including infection, bleeding, stent migration and stent occlusion, after LAMS placement between WON and PPC.Conclusions:Endoscopic ultrasound-guided LAMS for PFCs are feasible, effective with preferable technical and clinical success rates. The clinical effect of LAMS on PPC is slightly better than that of WON, but its adverse reactions still need to be verified in a large-sample prospective study.

简介：【摘要】 目的 观察颈部血管超声应用于缺血性脑血管病患者的临床价值。方法 选取我院2013年6月—2015年6月诊治的124例缺血性脑血管病发病初期患者为研究组，同期选取124例健康受检者为对照组。两组受检者均在常规检查的基础上进行颈部血管超声检查，观察两组受检者的动脉粥样硬化斑块、颈动脉狭窄、颈动脉中膜厚与脑血流动力学指标等情况。结果 研究组颈动脉与脑血流动力学指标情况均高于对照组，差异有统计学意义(P