Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas

Abstract

Background and purpose: Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes.

Materials and methods: Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions.

Results: Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies.

Conclusions: Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.

Fig 1.

An overview of the image-processing pipeline and model training and validation procedure to identify locations associated with survival. A, The image-processing pipeline is applied to both training (Stanford University Medical Center) and validation (TCGA) cohorts. B, Algorithm training identifies anatomic regions associated with survival, which is validated in the TCGA cohort. The training algorithm using the threshold-free cluster enhancement method takes as the input group labels dichotomized by survival outcome and the superimposed tumor heat map of the Stanford University Medical Center patient cohort analyzed in the image-processing pipeline; the pipeline outputs anatomic regions significantly associated with the 2 survival groups, which are used to classify the TCGA validation set into a poor survival group and a good survival group on the basis of tumor regions present or absent in the prognostic region.

Fig 2.

Axial, sagittal, and coronal section views of the region associated with poor survival in the training cohort (false discovery rate, P < .05). The cluster of voxels associated with poor survival was localized in the occipitotemporal periventricular white matter in the right hemisphere (right periatrial).

Fig 3.

Kaplan-Meier survival curves of patients with GBMs depict decreased overall survival in TCGA patients with an overlap (group I) versus nonoverlap (group II) with the voxels significantly associated with survival identified from the training cohort in the test cohort (log-rank test, P = .034).

Fig 4.

Axial postcontrast T1-weighted images of 4 patients from group II. A, A 69-year-old man with a right parietal GBM and an overall survival of 27 months. B, A 49-year-old man with a right temporal GBM and an overall survival of 25 months. C, A 63-year-old man with a left temporal GBM and an overall survival of 21 months. D, A 36-year-old woman with a left frontal GBM, an overall survival of 6 months, and the smallest tumor volume in the group of intermediate tumors.