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Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

ABSTRACT: Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor.To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby potentially delaying recurrence and prolonging survival.Preoperative multiparametric magnetic resonance images (T1, T1-gadolinium, T2-weighted, T2-weighted fluid-attenuated inversion recovery, diffusion tensor imaging, and dynamic susceptibility contrast-enhanced magnetic resonance images) from 31 patients were combined using machine learning methods, thereby creating predictive spatial maps of infiltrated peritumoral tissue. Cross-validation was used in the retrospective cohort to achieve generalizable biomarkers. Subsequently, the imaging signatures learned from the retrospective study were used in a replication cohort of 34 new patients. Spatial maps representing the likelihood of tumor infiltration and future early recurrence were compared with regions of recurrence on postresection follow-up studies with pathology confirmation.This technique produced predictions of early recurrence with a mean area under the curve of 0.84, sensitivity of 91%, specificity of 93%, and odds ratio estimates of 9.29 (99% confidence interval: 8.95-9.65) for tissue predicted to be heavily infiltrated in the replication study. Regions of tumor recurrence were found to have subtle, yet fairly distinctive multiparametric imaging signatures when analyzed quantitatively by pattern analysis and machine learning.Visually imperceptible imaging patterns discovered via multiparametric pattern analysis methods were found to estimate the extent of infiltration and location of future tumor recurrence, paving the way for improved targeted treatment.

SUBMITTER: Akbari H

PROVIDER: S-EPMC4853918 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

Akbari Hamed H Macyszyn Luke L Da Xiao X Bilello Michel M Wolf Ronald L RL Martinez-Lage Maria M Biros George G Alonso-Basanta Michelle M OʼRourke Donald M DM Davatzikos Christos C

Neurosurgery 20160401 4

<h4>Background</h4>Glioblastoma is an aggressive and highly infiltrative brain cancer. Standard surgical resection is guided by enhancement on postcontrast T1-weighted (T1) magnetic resonance imaging, which is insufficient for delineating surrounding infiltrating tumor.<h4>Objective</h4>To develop imaging biomarkers that delineate areas of tumor infiltration and predict early recurrence in peritumoral tissue. Such markers would enable intensive, yet targeted, surgery and radiotherapy, thereby po ...[more]

PMID: 26813856

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Project description:Purpose/objectivesMultiparametric advanced MR and [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) imaging may be important biomarkers for prognosis as well for distinguishing recurrent glioblastoma multiforme (GBM) from treatment-related changes.Methods/materialsWe retrospectively evaluated 30 patients treated with chemoradiation for GBM and underwent advanced MR and FDG-PET for confirmation of tumor progression. Multiparametric MRI and FDG-PET imaging metrics were evaluated for their association with 6-month overall (OS) and progression-free survival (PFS) based on pathological, radiographic, and clinical criteria.Results17 males and 13 females were treated between 2001 and 2014, and later underwent FDG-PET at suspected recurrence. Baseline FDG-PET and MRI imaging was obtained at a median of 7.5?months [interquartile range (IQR) 3.7-12.4] following completion of chemoradiation. Median follow-up after FDG-PET imaging was 10?months (IQR 7.2-13.0). Receiver-operator characteristic curve analysis identified that lesions characterized by a ratio of the SUVmax to the normal contralateral brain (SUVmax/NB index) >1.5 and mean apparent diffusion coefficient (ADC) value of ?1,400?×?10-6?mm2/s correlated with worse 6-month OS and PFS. We defined three patient groups that predicted the probability of tumor progression: SUVmax/NB index >1.5 and ADC ?1,400?×?10-6 mm2/s defined high-risk patients (n?=?7), SUVmax/NB index ?1.5 and ADC >1,400?×?10-6 mm2/s defined low-risk patients (n?=?11), and intermediate-risk (n?=?12) defined the remainder of the patients. Median OS following the time of the FDG-PET scan for the low, intermediate, and high-risk groups were 23.5, 10.5, and 3.8?months (p?<?0.01). Median PFS were 10.0, 4.4, and 1.9?months (p?=?0.03). Rates of progression at 6-months in the low, intermediate, and high-risk groups were 36, 67, and 86% (p?=?0.04).ConclusionRecurrent GBM in the molecular era is associated with highly variable outcomes. Multiparametric MR and FDG-PET biomarkers may provide a clinically relevant, non-invasive and cost-effective method of predicting prognosis and improving clinical decision making in the treatment of patients with suspected tumor recurrence.

Dataset Information

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

Publications

Imaging Surrogates of Infiltration Obtained Via Multiparametric Imaging Pattern Analysis Predict Subsequent Location of Recurrence of Glioblastoma.

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