Analysis of prognostic factors for survival in elderly patients with glioma / 中南大学学报(医学版)

Objective:To analyze the prognostic factors for survival in elderly patients with glioma.Methods:We performed a retrospective analysis of prognostic factors for elderly patients with glioma,who were treated by the same attending doctor during June 2014 and June 2016,to investigate the correlations of the age,dimension of pathology,histological grade,extent of resection,adjuvant therapy,preoperative Karnofsky Performance Scale (KPS) score,postoperative KPS score,molecular markers [isocitrate dehydrogenase-1 (IDHH-1),O6-methylguanine DNA-transferase (MGMT),epidermal growth factor receptor (EGFR),Ki-67] with the prognosis.Results:A total of 45 patients were included in the study.The median overall survival (OS) was 11 months.The median progression-free survival (PFS) was 6 months.Univariate analysis revealed that the age,gender,dimension ofpathology,histological grade and preoperative KPS score had no significant correlation with survival (P＞0.05).The gross total resection,higher postoperative KPS score,adjuvant therapy,lower Ki-67 index were significantly correlated with survival.The expressions of MGMT and EGFR were significant factors for survival.High postoperative KPS score (P=0.019),adjuvant therapy (P=0.024),and the expression of MGMT (P=0.026) were independent predictors for increased median OS in a multivariate regression model.Conclusion:The extent of resection,adjuvant therapy,postoperative KPS score and molecular markers are the influential factors for survival.Larger prospective studies are needed to confirm these findings.

A novel denoising approach to SVD filtering based on DCT and PCA in CT image / 生物医学工程学杂志

Because of various effects of the imaging mechanism, noises are inevitably introduced in medical CT imaging process. Noises in the images will greatly degrade the quality of images and bring difficulties to clinical diagnosis. This paper presents a new method to improve singular value decomposition (SVD) filtering performance in CT image. Filter based on SVD can effectively analyze characteristics of the image in horizontal (and/or vertical) directions. According to the features of CT image, we can make use of discrete cosine transform (DCT) to extract the region of interest and to shield uninterested region so as to realize the extraction of structure characteristics of the image. Then we transformed SVD to the image after DCT, constructing weighting function for image reconstruction adaptively weighted. The algorithm for the novel denoising approach in this paper was applied in CT image denoising, and the experimental results showed that the new method could effectively improve the performance of SVD filtering.