Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study

BackgroundThe 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.MethodsTaking 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.ResultsThe 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%).ConclusionsThe CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registrationChictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.     

基于Faster-RCNN的肺结节检测算法

针对目前的肺结节检测中存在的个体差异、同病异影、同影异病的问题,提出一种大样本条件下的基于Faster-RCNN的肺结节检测算法,对比研究目前的深度学习模型的适应性,给出一种通用的随着样本数量增加肺结节检测率持续提升的策略。首先搭建深度学习的软硬件环境,设置影像数据接口与Faster-RCNN的网络接口匹配;然后搭建Faster-RCNN的单类分类网络,并对网络结构的参数进行调整优化;最后用包含2 000例病人的肺结节数据集,通过不同的卷积神经网络模型 (包括ZF和VGG),计算CT图像在各自模型中的特征。对测试结果进行分析评估,分别统计其漏检率、检测准确率,并探讨不同训练数量和数据增广类型对最终检测准确率的影响。最终ZF模型的检测准确率为90.82%,准确率的波动方差为13.30%;VGG模型的检测准确率为87.02%,准确率的波动方差为37.10%。ZF模型的波动方差小,检测精确度高,综合考虑,ZF模型对肺结节的检测效果优于VGG模型的检出效果。所提出的肺结节检测技术具有良好的理论价值和工程应用价值。     

Efficient drug discovery by rational lead hybridization based on crystallographic overlay

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运动与ATP-敏感型钾离子通道

背景：在运动生理状态下，KATP 在调节冠状动脉张力、运动诱导心肌保护效应和延缓骨骼肌疲劳等多个方面具有重要作用。目的：对KATP在运动中的作用进行了综述和探讨，以期为深入了解运动调节机体代谢提供理论参考。方法：检索1991年1月至2014年6月 PubMed数据库及维普中文科技数据库文献。英文检索词为“KATP Channels；Adenosine Triphosphate；Sports；Myocardium；Ion Channels”，中文检索词为“KATP通道；三磷酸腺苷；运动；心肌；离子通道”。选择与KATP分子结构、生物学功能及调控相关，以及KATP与冠状动脉、心肌、骨骼肌疲劳及运动能力相关的文献42篇文献进行探讨。结果与结论：ATP敏感性钾离子通道可以偶联细胞内能量代谢和细胞膜兴奋性，在应对各种生理和病理应激时是保护心肌的效应器之一。长期的耐力训练则会增加骨骼肌和心肌KATP的表达，可能是心肌和骨骼肌对运动应激产生的一种适应性表现。KATP 可能参与冠状动脉血流量的调节。在运动诱导的减轻心肌缺血再灌注损伤的保护效应中，心肌KATP具有重要作用。当骨骼肌疲劳发生时，KATP的激活有利于防止ATP的过度消耗而造成肌纤维损伤和细胞死亡，有利于疲劳的快速恢复。关于KATP与运动能力的关系仍需进一步的研究。     

社区痴呆患者漫游行为非药物干预的研究进展

介绍社区痴呆患者漫游行为状况及其对健康的影响,漫游导致的不良后果严重影响患者的生活质量,降低其自理能力,给患者带来痛苦的同时,增加社会和家庭的医疗照护负担。老年痴呆患者漫游风险因素的评估工具主要有漫游风险量表、老年痴呆患者日常空间定向问卷、寻路效能量表;非药物干预措施主要包括环境改造、智能家居技术、参与活动、感觉疗法等,可改善社区痴呆患者漫游行为。