In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 73, No. 8_Supplement ( 2013-04-15), p. 2669-2669
Abstract:
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in humans. Patients diagnosed with GBM have a poor prognosis, with less than 25% of patients surviving more than two years. Despite intensive, multimodality treatment with extensive surgical resection, radiotherapy, and chemotherapy, recurrence of GBM is inevitable. Thus far GBM research has focused mainly on classification of tumor subtypes, segmentation of enhancing and non-enhancing tumor tissue on MRI, and distinguishing treatment-related effects from recurrence. The purpose of this research is to utilize MR perfusion imaging along with advanced image analysis methods to predict specific areas of future recurrence in GBM. Forty patients with glioblastoma (WHO Grade IV), who subsequently experience recurrence, were utilized for this study. T1, T1CE, T2, FLAIR, and perfusion images were co-registered and regions of interest (ROIs) were drawn for each subject on imaging obtained prior to surgery in white matter, gray matter, CSF, edema, enhancing tumor, non-enhancing tumor and necrosis. Principal component analysis (PCA) was then employed to extract the uncorrelated variables that reflect the temporal dynamics of perfusion. Leave-one-out cross-validation was used when building the PCA model from a training set and testing it on new patients. The results demonstrate marked separation between edematous peritumoral regions and peritumoral regions that later recurred. Hence, this study indicates that imaging biomarkers predictive of tumor recurrence can be constructed using advanced imaging and analysis methods, potentially leading to the creation of a novel, and important, clinical tool. Citation Format: Luke Macyszyn, Hamed Akbari, Xiao Da, Ragini Verma, Ronald Wolf, Michel Bilello, Elias Melhem, Donald O'Rourke, Christos Davatzikos. Predicting glioblastoma recurrence using novel analysis of perfusion MRI. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2669. doi:10.1158/1538-7445.AM2013-2669 Note: This abstract was not presented at the AACR Annual Meeting 2013 because the presenter was unable to attend.
Type of Medium:
Online Resource
ISSN:
0008-5472
,
1538-7445
DOI:
10.1158/1538-7445.AM2013-2669
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2013
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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