IntroductionGlioblastoma is characterized by its remarkable heterogeneity and dismal prognosis. Histogram analysis of quantitative magnetic resonance imaging (MRI) is an important in vivomethod to study intratumoral heterogeneity. With large amounts of histogram features generated, integrating these modalities effectively for clinical decision remains a challenge. MethodsA total of 80 patients with supratentorial primary glioblastoma were recruited. All patients received surgery and standard regimen of temozolomide chemoradiotherapy. Diagnosis was confirmed by pathology. Anatomical T2-weighted, T1-weighted post-contrast and FLAIR images, as well as dynamic susceptibility contrast (DSC), diffusion tensor imaging (DTI) and chemical shift imaging were acquired preoperatively using a 3T MRI scanner. DTI-p, DTI-q, relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated. Contrast-enhancing (CE) and non-enhancing (NE) regions of interest were manually delineated. Voxel intensity histograms were constructed from the CE and NE regions independently. Patient clustering was performed by the Multi-View Biological Data Analysis (MVDA) approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of the patient clustering to survival. The histogram features selected from MVDA approach were evaluated using receiver operator characteristics (ROC) curve analysis. The metabolic signatures of the patient clusters were analyzed by multivoxel MR spectroscopy (MRS).All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/235861 doi: bioRxiv preprint first posted online Dec. 18, 2017; ResultsThe MVDA approach yielded two final patient clusters, consisting of 53 and 27 patients respectively. The two patient subgroups showed significance for overall survival (p = 0.007, HR = 0.32) and progression-free survival (p < 0.001, HR = 0.33) in multivariate Cox regression analysis. Among the features selected by MVDA, higher mean value of DTI-q in the non-enhancing region contributed to a worse OS (HR = 1.40, p = 0.020) and worse PFS (HR = 1.36, p = 0.031). Multivoxel MRS showed N-acetylaspartate/creatine (NAA/Cr) ratio between the two clusters, both in the CE region (p < 0.001) and NE region (p = 0.013).Glutamate/Cr (Glu/Cr) ratio and glutamate + glutamine/Cr (Glx/Cr) of the cluster 1 was significantly lower than cluster 2 (p = 0.037, and 0.027 respectively) In the NE region. DiscussionThis study demonstrated that integrating multi-parametric and multi-regional MRI histogram features may help to stratify patients. The histogram features selected from the proposed approach may be used as potential imaging markers in personalized treatment strategy and response determination.Key words: Glioblastoma, heterogeneity, magnetic resonance i...
Introduction. Glioblastoma exhibits profound tumor heterogeneity, which causes
Abstracts iii8NEURO-ONCOLOGY • MAY 2017 EOR (p<0.001), KPS (p<0.001) and tumor side (p<0.02) significantly influenced OS. In all cases of complete enhancing resection, a Flair RTV > 0 cc adversely affected PFS and OS. CONCLUSIONS: supratotal resection with complete excision of enhancing and Flair tumor components could represent a safe and feasible treatment for newly diagnosed GBMs, strongly influencing survival. INTRODUCTION: The identification of the glioblastoma invasive margin in the peritumoral zone outside of the contrast enhancing MRI is difficult, which leads to the failure of local treatment control. We aimed to use radiomics features to characterize the peri-tumoral areas, focusing on the difference between the tumor progression area and non-progression area by using preoperative multi-modal MRIs. And further identify the invasive margin of the glioblastoma. METHODS: We retrospectively included 51 newly diagnosed cerebral glioblastoma patients. All patients were treated with 5-aminolevulinic acid (5-ALA) fluorescence guidance surgery and standard postoperative concomitant chemoradiotherapy. Preoperative MRI data acquisition was performed using a 3T MRI. Imaging included volumetric post contrast T1-weighted, perfusion MR, and DTI. DTI was decomposed into isotropic (p) component and anisotropic (q) components. The peritumoral progression areas were obtained by coregistration of the known progression images to the preoperative MRI by using a two stage semi-automatic coregistration method. A total number of 294 voxel based radiomics features were extracted from difference sequences, included first order features, and texture features by using an in-house Matlab program. 37 patients were used to train the classifier, and the other 20 patients were used as an independent validation cohort. RESULTS: In the peritumoral progression area, compare to non-progression area within 10 mm around the contrast enhancing lesion, there were higher signal intensity in FLAIR (p = 0.02), rCBV (p = 0.038), and T1C (p = 0.0004), and there were lower intensity in ADC (p = 0.029) and p (p = 0.001). Radiomics features 35 first order features and 77 second order features were found significantly different between progression and non-progression area. By using supervised convoluted neural network, there was an overall accuracy of 92.4% in the training set (n = 37) and 78.5% in the validation set CONCLUSION: Multimodal MR imaging, particularly diffusion tensor imaging, can demonstrate distinct characteristics in areas of potential progression on preoperative MRI. Besides, radiomics features can be a potential useful tool to further identify the tumor invasive margin. OS04.5 RADIOMICS FEATURES CAN CHARACTERIZE
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