• DocumentCode
    2131716
  • Title

    Prediction of renal transplant rejection and acute tubular necrosis in renal transplant based on SVM

  • Author

    Xun Li ; Yao Wang ; Chengxuan Wang ; Sanqing Hu ; Ying Xu ; Fei Han ; Jianghua Chen

  • Author_Institution
    Inst. of Comput. Applic., Hangzhou Dianzi Univ., Hangzhou, China
  • fYear
    2012
  • fDate
    16-18 Oct. 2012
  • Firstpage
    577
  • Lastpage
    581
  • Abstract
    Prevention and proper treatment of renal transplant rejection and acute tubular necrosis in kidney are the key to improving the long-term kidney transplant survival rate. Hence, it is important to predict the acute renal graft rejection in early stage. In recent years, there emerged some biomarkers measured through non-invasive techniques that may indicate the acute rejection. In this paper, we apply SVM method to analyze biomarkers, medullary R2* (MR2*) and cortical R2* (CR2*) in transplanted kidney, acquired through BOLD MRI for classification of patients with normally functioning kidney transplants and acute rejection in kidney, including acute allograft rejection and acute tubular necrosis. Furthermore, we use the classification model to predict the acute kidney rejection. The results show that the application of SVM in the analysis of CR2* and MR2* has its potential in prediction of acute rejection in kidney.
  • Keywords
    biomedical MRI; diseases; kidney; patient treatment; support vector machines; BOLD MRI; SVM method; acute renal graft rejection; acute tubular necrosis; biomarker; cortical analysis; kidney transplant survival rate; magnetic resonance imaging; medullary analysis; patient classification model; renal transplant rejection; acute tubular necrosis; prediction; renal transplant rejection; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biomedical Engineering and Informatics (BMEI), 2012 5th International Conference on
  • Conference_Location
    Chongqing
  • Print_ISBN
    978-1-4673-1183-0
  • Type

    conf

  • DOI
    10.1109/BMEI.2012.6512936
  • Filename
    6512936