Computer-aided diagnosis of isocitrate dehydrogenase genotypes in glioblastomas from radiomic patterns

Chung Ming Lo, Rui Cian Weng, Sho Jen Cheng, Hung Jung Wang, Kevin Li Chun Hsieh, Jianxun Ding

研究成果: 雜誌貢獻文章同行評審

2 引文 斯高帕斯(Scopus)

摘要

World Health Organization tumor classifications of the central nervous system differentiate glioblastoma multiforme (GBM) into wild-type (WT) and mutant isocitrate dehydrogenase (IDH) genotypes. This study proposes a noninvasive computer-aided diagnosis to interpret the status of IDH in glioblastomas from transformed magnetic resonance imaging patterns. The collected image database was composed of 32 WT and 7 mutant IDH cases. For each image, a ranklet transformation which changed the original pixel values into relative coefficients was 1st applied to reduce the effects of different scanning parameters and machines on the underlying patterns. Extracting various textural features from the transformed ranklet images and combining them in a logistic regression classifier allowed an IDH prediction. We achieved an accuracy of 90%, a sensitivity of 57%, and a specificity of 97%. Four of the selected textural features in the classifier (homogeneity, difference entropy, information measure of correlation, and inverse difference normalized) were significant (P<.05), and the other 2 were close to being significant (P=.06). The proposed computer-aided diagnosis system based on radiomic textural features from ranklet-transformed images using relative rankings of pixel values as intensity-invariant coefficients is a promising noninvasive solution to provide recommendations about the IDH status in GBM across different healthcare institutions.
原文英語
文章編號e19123
期刊Medicine (United States)
99
發行號8
DOIs
出版狀態已發佈 - 二月 2020

ASJC Scopus subject areas

  • Medicine(all)

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