Dataset Information

Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients.

ABSTRACT:

Background

Radiological characteristics may reflect the biological features of brain tumors and may be associated with genetic alterations that occur in tumorigenesis. This study aimed to investigate the relationship between radiological features and IDH1 status as well as their predictive value for survival of glioblastoma patients.

Methods

The clinical information and MR images of 280 patients with histologically confirmed glioblastoma were retrospectively reviewed. The radiological characteristics of tumors were examined on MR images, and the IDH1 status was determined using DNA sequencing for all cases. The Kaplan-Meier method and Cox regression model were used to identify prognostic factors for progression-free and overall survival.

Results

The IDH1 mutation was associated with longer progression-free survival (P = .022; hazard ratio, 0.602) and overall survival (P = .018; hazard ratio, 0.554). In patients with the IDH1 mutation, tumor contrast enhancement and peritumoral edema indicated worse progression-free survival (P = .015 and P = .024, respectively) and worse overall survival (P = .024 and P = .032, respectively). For tumors with contrast enhancement, multifocal contrast enhancement of the tumor lesion was associated with poor progression-free survival (P = .002) and poor overall survival (P = .010) in patients with wild-type IDH1 tumors.

Conclusions

Combining the radiological features and IDH1 status of a tumor allows more accurate prediction of survival outcomes in glioblastoma patients. The complementary roles of genetic changes and radiological features of tumors should be considered in future studies.

SUBMITTER: Wang K

PROVIDER: S-EPMC4799681 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Publications

Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients.

Wang Kai K Wang Yinyan Y Fan Xing X Wang Jiangfei J Li Guilin G Ma Jieling J Ma Jun J Jiang Tao T Dai Jianping J

Neuro-oncology 20150925 4

<h4>Background</h4>Radiological characteristics may reflect the biological features of brain tumors and may be associated with genetic alterations that occur in tumorigenesis. This study aimed to investigate the relationship between radiological features and IDH1 status as well as their predictive value for survival of glioblastoma patients.<h4>Methods</h4>The clinical information and MR images of 280 patients with histologically confirmed glioblastoma were retrospectively reviewed. The radiolog ...[more]

PMID: 26409566

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Project description:Glioblastoma (GBM) is the most common form of brain tumor and has a uniformly poor prognosis. Development of prognostic biomarkers in easily accessible serum samples have the potential to improve the outcomes of patients with GBM through personalized therapy planning. In this study pre-treatment serum samples from 30 patients newly diagnosed with GBM were evaluated using a 40-protein multiplex ELISA platform. Analysis of potentially relevant gene targets using The Cancer Genome Atlas database was done using the Glioblastoma Bio Discovery Portal (GBM-BioDP). A ten-biomarker subgroup of clinically relevant molecules was selected using a functional grouping analysis of the 40 plex genes with two genes selected from each group on the basis of degree of variance, lack of co-linearity with other biomarkers and clinical interest. A Multivariate Cox proportional hazard approach was used to analyze the relationship between overall survival (OS), gene expression, and resection status as covariates. Thirty of 40 of the MSD molecules mapped to known genes within TCGA and separated the patient cohort into two main clusters centered predominantly around a grouping of classical and proneural versus the mesenchymal subtype as classified by Verhaak. Using the values for the 30 proteins in a prognostic index (PI) demonstrated that patients in the entire cohort with a PI below the median lived longer than those patients with a PI above the median (HR 1.8, p=0.001) even when stratified by both age and MGMT status. This finding was also consistent within each Verhaak subclass and highly significant (range p=0.0001-0.011). Additionally, a subset of ten proteins including, CRP, SAA, VCAM1, VEGF, MDC, TNFA, IL7, IL8, IL10, IL16 were found to have prognostic value within the TCGA database and a positive correlation with overall survival in GBM patients who had received gross tumor resection followed by conventional radiation therapy and temozolomide treatment concurrent with the addition of valproic acid. These findings demonstrate that proteomic approaches to the development of prognostic assays for treatment of GBM may hold potential clinical value.

Dataset Information

Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients.

Background

Methods

Results

Conclusions

Publications

Radiological features combined with IDH1 status for predicting the survival outcome of glioblastoma patients.

Similar Datasets

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