Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) images.Methods: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ performance.Results: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to varying degrees with MDCS assistance.Conclusions: MDCS demonstrated excellent performance in the detection and classification of breast lesions,and greatly enhanced the overall performance of radiologists.

Clinical Features of Severe Tsutsugamushi Disease Complicated with Hemophagocytic Lymphohistiocytosis

The article discusses a case of severe tsutsugamushi disease complicated with hemophagocytic syndrome in Guangdong Maternal and Child Health Hospital and concludes that blood purification technology has significant therapeutic effect among children with severe HLH complicated with multiple organ dysfunction.

Management Algorithm for Prevention of Mother-to-child Transmission of Hepatitis B Virus(2022)

The World Health Organization(WHO)has set the goal of eliminating hepatitis as a threat to public health by 2030.Blocking mother-to-child transmission(MTCT)of hepatitis B virus(HBV)is not only the key to eliminating viral hepatitis,but also a hot issue in the field of hepatitis B prevention and treatment.To standardize the clinical management of preventing MTCT of HBV and achieve zero HBV infection among infants,the Chinese Foundation for Hepatitis Prevention and Control organized experts to compile a management algorithm for prevention of MTCT of HBV based on the latest research progress and guidelines,including 10 steps of pregnancy management and postpartum follow-up,among which screening,antiviral treatment,and infant immunization are its core components.