Magnetic induction hyperthermia becomes a very important tumor treatment method at present. In order to ensure a successful operation, doctors should make hyperthermia treatment planning before surgery. Based on Integration Healthcare Enterprise (IHE) framework and Digital Imaging and Communications in Medcine (DICOM) standard, we proposed and carried out a network workflow integrated with modern medical information systems for the dissemination of information in magnetic induction hyperthermia like accurate accessing patient information and radiology image data, storing processed images, sharing and verifying hyperthermia reports. The results proved that our system could not only improve the efficiency of magnetic induction hyperthermia treatment planning, but also save medical resources and reduce labor costs.
Intensity-modulated radiotherapy planning for nasopharyngeal carcinoma is very complex. The quality of plan is often closely linked to the experience of the treatment planner. In this study, 10 nasopharyngeal carcinoma patients at different stages were enrolled. Based on the scripting of Pinnacle3 9.2 treatment planning system, the computer program was used to set the basic parameters and objective parameters of the plans. At last, the nasopharyngeal carcinoma intensity-modulated radiotherapy plans were completed automatically. Then, the automatical and manual intensity-modulated radiotherapy plans were statistically compared and clinically evaluated. The results showed that there were no significant differences between those two kinds of plans with respect to the dosimetry parameters of most targets and organs at risk. The automatical nasopharyngeal carcinoma intensity-modulated radiotherapy plans can meet the requirements of clinical radiotherapy, significantly reduce planning time, and avoid the influence of human factors such as lack of experience to the quality of plan.
In order to satisfy demands of massive and heterogeneous tumor clinical data processing and the multi-center collaborative diagnosis and treatment for tumor diseases, a Tumor Data Interacted System (TDIS) was established based on grid platform, so that an implementing virtualization platform of tumor diagnosis service was realized, sharing tumor information in real time and carrying on standardized management. The system adopts Globus Toolkit 4.0 tools to build the open grid service framework and encapsulats data resources based on Web Services Resource Framework (WSRF). The system uses the middleware technology to provide unified access interface for heterogeneous data interaction, which could optimize interactive process with virtualized service to query and call tumor information resources flexibly. For massive amounts of heterogeneous tumor data, the federated stored and multiple authorized mode is selected as security services mechanism, real-time monitoring and balancing load. The system can cooperatively manage multi-center heterogeneous tumor data to realize the tumor patient data query, sharing and analysis, and compare and match resources in typical clinical database or clinical information database in other service node, thus it can assist doctors in consulting similar case and making up multidisciplinary treatment plan for tumors. Consequently, the system can improve efficiency of diagnosis and treatment for tumor, and promote the development of collaborative tumor diagnosis model.
The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People’s Republic of China has issued seven trial versions of the “Guidelines for the Diagnosis and Treatment of COVID-19”. However, it is difficult for clinicians and laymen to quickly and accurately distinguish the similarities and differences among the different versions and locate the key points of the new version. This paper reports a computer-aided intelligent analysis method based on machine learning, which can automatically analyze the similarities and differences of different treatment plans, present the focus of the new version to doctors, reduce the difficulty in interpreting the “diagnosis and treatment plan” for the professional, and help the general public better understand the professional knowledge of medicine. Experimental results show that this method can achieve the topic prediction and matching of the new version of the program text through unsupervised learning of the previous versions of the program topic with an accuracy of 100%. It enables the computer interpretation of “diagnosis and treatment plan” automatically and intelligently.
In order to understand the evolution of the diagnosis and treatment plans of corona virus disease 2019 (COVID-19), and provide convenience for medical staff in actual diagnosis and treatment, this paper uses the 9 diagnosis and treatment plans of COVID-19 issued by the National Health Commission during the period from January 26, 2020 to August 19, 2020 as research data to perform comparative analysis and visual analysis. Based on text mining, this paper obtained the text similarity and summarized its evolution law by expressing and measuring the similarity of the overall diagnosis and treatment plans of COVID-19 and the same modules, which provides reference for clinical diagnosis and treatment practice and other diagnosis and treatment plan formulation.