High-performance computational analysis of glioblastoma pathology images with database support identifies molecular and survival correlates

Jun Kong, Fusheng Wang, George Teodoro, Lee Cooper, Carlos S. Moreno, Tahsin Kurc, Tony Pan, Joel Saltz, Daniel Brat
2013 2013 IEEE International Conference on Bioinformatics and Biomedicine  
In this paper, we present a novel framework for microscopic image analysis of nuclei, data management, and high performance computation to support translational research involving nuclear morphometry features, molecular data, and clinical outcomes. Our image analysis pipeline consists of nuclei segmentation and feature computation facilitated by high performance computing with coordinated execution in multi-core CPUs and Graphical Processor Units (GPUs). All data derived from image analysis are
more » ... managed in a spatial relational database supporting highly efficient scientific queries. We applied our image analysis workflow to 159 glioblastomas (GBM) from The Cancer Genome Atlas dataset. With integrative studies, we found statistics of four specific nuclear features were significantly associated with patient survival. Additionally, we correlated nuclear features with molecular data and found interesting results that support pathologic domain knowledge. We found that Proneural subtype GBMs had the smallest mean of nuclear Eccentricity and the largest mean of nuclear Extent, and MinorAxisLength. We also found gene expressions of stem cell marker MYC and cell proliferation maker MKI67 were correlated with nuclear features. To complement and inform pathologists of relevant diagnostic features, we queried the most representative nuclear instances from each patient population based on genetic and transcriptional classes. Our results demonstrate that specific nuclear features carry prognostic significance and associations with transcriptional and genetic classes, highlighting the potential of high throughput pathology image analysis as a complementary approach to human-based review and translational research.
doi:10.1109/bibm.2013.6732495 pmid:25098236 pmcid:PMC4120024 dblp:conf/bibm/KongWTCMKPSB13 fatcat:bavl4tbe6jgz5fcuvufcg4cxrq