Date Published: January 31, 2019
Publisher: Impact Journals
Author(s): Xing Liu, Yiming Li, Shaowu Li, Xing Fan, Zhiyan Sun, Zhengyi Yang, Kai Wang, Zhong Zhang, Tao Jiang, Yong Liu, Lei Wang, Yinyan Wang.
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.
Diffuse gliomas, graded from II to IV according to the World Health Organization (WHO) criteria, are the most common and lethal primary tumors of the central nervous system. Lower grade gliomas (LGGs), designated as astrocytomas, oligodendrogliomas, and mixed oligoastrocytomas of grade II and III gliomas, account for approximately 43.2% of all gliomas diagnosed in adults [1–3]. Although LGGs have a relatively better therapeutic response and longer overall survival (OS) than fully malignant glioblastomas (GBM, WHO grade IV), they eventually transform to higher grade tumors with greater mortality [4, 5].
By assessing the comprehensive characteristics of the entire tumor noninvasively, MR imaging is currently an indispensable approach for glioma diagnosis and treatment monitoring. The development of computational methodologies has successfully converted routine MR images to informative descriptors, substituting a quantitative and objective modality for traditionally qualitative and subjective methods. In the present study, we analyzed 431 T2-weighted radiomic features in 158 LGG patients and identified an IDH-specific radiomic signature. An integrated analysis of both radiomic and transcriptomic data indicated that these radiomic features could reflect the tumor immune response, adhesion, and several malignant biological processes, all of which are in accord with behaviors that differentiate between IDHMUT and IDHWT LGGs. Furthermore, these IDH-specific radiomic features could be utilized to establish a prognostic evaluation model. The IDHMUT patients with a low risk score showed a significantly longer OS than the IDHMUT patients with a high-risk score.
In conclusion, we demonstrated that radiomic features could serve as an alternative approach for IDH phenotype classification in LGG patients. The MR imaging is a routine examination for gliomas, and quantitative radiomic and radiogenomic analyses can potentially provide a noninvasive modality for prognosis prediction and phenotypic monitoring. As the whole process of radiomic analysis takes less than 10 minutes for an individual case and can be practice automatically with out further cost, this newly developed technique is increasingly applied to assisting clinical diagnosis and decision making.