Eqrthropoieais-Stimulating Agents In The Management Of Cancer Patients With Anemia:a Meta-Analysis And Automatic Recognition Of The Blood Cells And Pathological Analysis

Aim:Erythropoiesis-stimulating agents (ESAs) are widely used in the management of anemia in cancer patients. Despite their apparent effectiveness, recent studies have suggested that use of ESAs could result in serious adverse events and even higher mortality. The general aim of the current study was to provide valuable information guiding clinicians to apply ESAs for the patients in a good way and preventing from SEA-associated serious events by systemic evaluation of the benefits and risks of ESAs for cancer patients with anemia.Methods:The initial literature search covered Medline, PubMed, Embase, and the Cochrane Center Register of Controlled Trials, and identified1569papers that could be used as potential studies. The meta-analysis included eight randomized controlled trials (n=2387) comparing ESA versus placebo in cancer patients with anemia. The evaluation included hemoglobin (Hb) response, blood transfusion rate and adverse events, such as venous thromboemblism (VTE), hypertension, on-study mortality, and cancer progression. The results are expressed as pooled odds ratio (OR). Publication bias was assessed using funnel plot.Results:ESAs significantly increased the Hb concentration (OR7.85,95%CI5.85to10.53, p＜0.001), and reduced the red blood cell (RBC) transfusion rate (OR0.52,95%CI0.42to0.65, p＜0.001). When the target Hb was no more than13g/dl, ESAs did not increase the accumulated adverse events (OR0.95, P=0.82), the on-study mortality (OR1.09, P=0.47) and VTE (OR1.58, P=0.13). Conclusions:ESAs improves anemia and reduces the RBC transfusion frequency. ESAs are not associated with increased risks for VTE and cancer progression, and increased on-study mortality in cancer patients with anemia when the target Hb value is no more than13g/dl. Aim:Acquiring the microscopic images of blood cells, using image processing and pattern recognition technology, the images were preprocessed and their features were extracted, aiming to develop a set of blood cells automatic recognition system. Based on these works the efficient and accurate automatic analysis instrument of blood cells detection and hematopathology can be developed and manufactured. And these works will contribute directly to the hematopathology automatic analysis.Methods:1) Collecting blood cell image dataset;2) Analyzing blood cell image color space model, based on color space transformation and adaptive binarization algorithm, an automatic segmentation and detection algorithm for blood cells images were proposed. This method can weaken the effect of light on the blood cells image processing effectively when used to segment leukocytes images.3) The feature parameters were extracted from three aspects: texture, shape, and color, after segmenting leukocytes images.4) Studying the application of support vector machine (SVM) algorithm in blood cells recognition, multiple classification recognition were used on leukocytes images based on strategy tree, then the hematopathology analysis was associated with cell count.5) The cell automatic identification system were tested and verified in Matlab environment.Results:A high accuracy and low time cost method of automatically detecting the blood cells was proposed.Conclusion:A set of peripheral blood cell automatic identification system was completed. The software runs efficiently and stably.