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Objective:To investigate the feasibility of deep learning radiomics model in the prediction of neoadjuvant chemotherapy (NAC) response in breast cancer based on ultrasound images at an early stage.Methods:Between January 2018 and June 2021, 218 patients with breast cancer who underwent NAC were enrolled in the retrospective study. All patients received a full cycle of NAC before surgery and underwent standard ultrasound examination before NAC and after the second cycles of NAC. Of all the patients, 166 patients came from institution 1 (the First Affiliated Hospital of Nanjing Medical University) were allocated into a primary cohort.Based on the architecture of Resnet 50 convolutional neural, a deep learning prediction model was built.Further validation was performed in an external testing cohort ( n=52) from institution 2 (General Hospital of Eastern Theater Command, PLA). The clinical model was constructed using independent clinical variables. To evaluate the predictive performance, areas under the curve (AUCs) of these models and two radiologists were compared by using the DeLong method. Results:The Resnet 50 model predicted the response of NAC with accuracy. The deep learning model, achieving an AUC of 0.923 (95% CI=0.884-0.962) in the primary cohort and an AUC of 0.896 (95% CI=0.807-0.980) in the test cohort, outperformed the clinical model and also performed better than two radiologists′ prediction (all P<0.05). Furthermore, the two radiologists achieved a better predictive efficacy (AUC 0.832 and 0.808 for radiologists 1 and 2, respectively) when assisted by the DL model (all P<0.01). Conclusions:The deep learning radiomics model is able to predict therapy response in the early-stage of NAC for breast cancer patients, which could guide clinicians and provide benefit for timely treatment strategy adjustment.
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The construction of medical knowledge platform is a core value of the intelligent construction of electronic medical records. The hospital-wide knowledge base construction covers a wide range of content, including multiple healthcare scenarios such as medicine, testing, inspection, surgery, blood transfusion and nursing. This article introduced how Jiangsu Province People′s Hospital used knowledge graphs and rule engine to construct a hospital knowledge management platform, realize the integration of knowledge-based knowledge base and a non-knowledge-based knowledge base, and embed clinical diagnosis and treatment rules into the information system for different flexible application scenarios.Finally, a multi-dimensional knowledge base was formed to realize the unified knowledge information integration of various clinical expert knowledge, and to provide integrated display and decision support for all departments, as well as realizing real-time data verification, prompting and control in each link.
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Electronic data capture(EDC) plays an important role in improving the quality of clinical research.The authors introduced the main functions of EDC and the use flow, then from its core function, analyzed the role of EDC in improving the quality of clinical research and scientific research management. Then they proposed the thinking of finding and solving problems from EDC " big data".Their efforts aim at enabling research administrators in extending clinical research management scope and management quality.
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Based on the new trend of transition from information service to knowledge service currently emerging in the field of information service,the paper presents the construction of a knowledge service system based on clinical data center.It introduces the architecture of the system,analyzes its features,describes its application effect and discusses the issues to be concerned for the further development of the knowledge service system in the future.