  Recent analysis identified distinct genomic subtypes of lower-grade glioma
tumors which are associated with shape features. In this study, we propose a
fully automatic way to quantify tumor imaging characteristics using deep
learning-based segmentation and test whether these characteristics are
predictive of tumor genomic subtypes. We used preoperative imaging and genomic
data of 110 patients from 5 institutions with lower-grade gliomas from The
Cancer Genome Atlas. Based on automatic deep learning segmentations, we
extracted three features which quantify two-dimensional and three-dimensional
characteristics of the tumors. Genomic data for the analyzed cohort of patients
consisted of previously identified genomic clusters based on IDH mutation and
1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and
microRNA expression. To analyze the relationship between the imaging features
and genomic clusters, we conducted the Fisher exact test for 10 hypotheses for
each pair of imaging feature and genomic subtype. To account for multiple
hypothesis testing, we applied a Bonferroni correction. P-values lower than
0.005 were considered statistically significant. We found the strongest
association between RNASeq clusters and the bounding ellipsoid volume ratio
($p<0.0002$) and between RNASeq clusters and margin fluctuation ($p<0.005$). In
addition, we identified associations between bounding ellipsoid volume ratio
and all tested molecular subtypes ($p<0.02$) as well as between angular
standard deviation and RNASeq cluster ($p<0.02$). In terms of automatic tumor
segmentation that was used to generate the quantitative image characteristics,
our deep learning algorithm achieved a mean Dice coefficient of 82% which is
comparable to human performance.
