IDH mutation-specific radiomic signature in lower-grade gliomas
Author(s) -
Xing Liu,
Yiming Li,
Shaowu Li,
Xing Fan,
Zhiyan Sun,
Zhengyi Yang,
Kai Wang,
Zhang Zhong,
Tao Jiang,
Yong Liu,
Lei Wang,
Yinyan Wang
Publication year - 2019
Publication title -
aging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 90
ISSN - 1945-4589
DOI - 10.18632/aging.101769
Subject(s) - isocitrate dehydrogenase , glioma , radiomics , mutant , subtyping , logistic regression , mutation , idh1 , biology , computational biology , oncology , medicine , cancer research , computer science , genetics , gene , radiology , biochemistry , enzyme , programming language
Unravelling the heterogeneity is the central challenge for glioma precession oncology. In this study, we extracted quantitative image features from T2-weighted MR images and revealed that the isocitrate dehydrogenase ( IDH ) wild type and mutant lower grade gliomas (LGGs) differed in their expression of 146 radiomic descriptors. The logistic regression model algorithm further reduced these to 86 features. The classification model could discriminate the two types in both the training and validation sets with area under the curve values of 1.0000 and 0.9932, respectively. The transcriptome-radiomic analysis revealed that these features were associated with the immune response, biological adhesion, and several malignant behaviors, all of which are consistent with biological processes that are differentially expressed in IDH wild type and IDH mutant LGGs. Finally, a prognostic signature showed an ability to stratify IDH mutant LGGs into high and low risk groups with distinctive outcomes. By extracting a large number of radiomic features, we identified an IDH mutation-specific radiomic signature with prognostic implications. This radiomic signature may provide a way to non-invasively discriminate lower-grade gliomas as with or without the IDH mutation.
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