Pseudo progression identification of glioblastoma with dictionary learning
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- 作者：Jian Zhanga ; Hengyong Yub ; Xiaohua Qiana ; Keqin Liua ; Hua Tana ; Tielin Yangc ; Maode Wangd ; King Chuen Lia ; Michael D. Chane ; Waldemar Debinskie ; Anna Paulssone ; Ge Wangf ; Xiaobo Zhoua ; xizhou@wakehealth.edu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Pseudo progression ; Glioblastoma multiforme ; Dictionary learning ; Genomics analysis ; Fractional anisotropy (FA) ; Diffusion tensor imaging (DTI)
- 刊名：Computers in Biology and Medicine
- 出版年：2016
- 出版时间：1 June 2016
- 年：2016
- 卷：73
- 期：Complete
- 页码：94-101
- 全文大小：3454 K
文摘
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Discriminating between the pseudo progression and the true progression of Glioblastoma with AUC of 0.87.
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Dictionary learning developed to process the DTI/FA volumetric images.
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A feature selection approach for the large number of sparse representation calculated by dictionary learning.
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ROI segmentation is not needed.
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Location restriction regarding the tumor position has been solved.