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Identifying High-Risk Tumor Volume Based On Multi-Region and Integrated Analysis of Multi-Parametric MR Images for Prognostication of Glioblastoma

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X Ren

X Ren1*, Y Cui2 , H Gao1 , R Li2 , (1) Shanghai Jiao Tong University, Shanghai, Shanghai, (2) Stanford University, Palo Alto, CA


TU-D-207B-4 (Tuesday, August 2, 2016) 11:00 AM - 12:15 PM Room: 207B

Purpose: To identify high-risk tumor volume for predicting survival in patients with glioblastoma on the basis of multi-region and integrated analysis of multi-parametric MR images.

Methods: In this retrospectively study, 34 patients with glioblastoma from the Cancer Imaging Archive (TCIA) were analyzed. The patients were included if all of the pre-operative 1) T1-weighted contrast-enhanced (T1c), 2) T2-weighted fluid-attenuation inversion recovery (FLAIR), and 3) diffusion-weighted (DW) MR images were available. The apparent diffusion coefficient (ADC) map was calculated from the DW images. Both the FLAIR and ADC images were co-registered onto the T1c image by rigid transformation. For each patient, gross tumor volume (GTV) was first semi-automatically delineated with a cell automation and level-set evolution algorithm. To fully capture the intrinsic intra-tumor heterogeneity of glioblastoma reflected on multi-parametric MR images, we further segmented the delineated tumor into several spatially distinct and phenotypically consistent subregions using k-means clustering, with T1c, FLAIR, and ADC voxel intensities as input features. The optimal number of clusters was determined based on the Calinski-Harabasz statistic. Finally, tumor volumes associated with potentially high-risk subregions were evaluated in terms of overall survival (OS) prediction.

Results: Three tumor subregions were identified within each glioblastoma. The tumor volume associated with the subregion of the lowest mean ADC values was prognostic of OS, with a concordance index (CI) of 0.648 (log-rank P=0.005, hazard ratio=3.82) and outperforming GTV (CI=0.627, log-rank P=0.019, hazard ratio=3.04). Less or similar prognostic performances were achieved for tumor volumes associated with subregions of the highest mean T1c intensities (CI=0.607, log-rank P=0.035, hazard ratio=2.57) and the highest mean FLAIR intensities (CI=0.613, log-rank P=0.014, hazard ratio=3.08).

Conclusion: Multi-region, integrated analysis of multi-parametric MRI identified high-risk tumor volume in glioblastoma. Integration of functional imaging with conventional MR sequences can potentially improve prediction of survival.

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