  Gliomas with CDKN2A mutations are known to have worse prognosis but imaging
features of these gliomas are unknown. Our goal is to identify CDKN2A specific
qualitative imaging biomarkers in glioblastomas using a new informatics
workflow that enables rapid analysis of qualitative imaging features with
Visually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. Sixty
nine patients undergoing GBM resection with CDKN2A status determined by
whole-exome sequencing were included. GBMs on magnetic resonance images were
automatically 3D segmented using deep learning algorithms incorporated within
PACS. VASARI features were assessed using FHIR forms integrated within PACS.
GBMs without CDKN2A alterations were significantly larger (64% vs. 30%,
p=0.007) compared to tumors with homozygous deletion (HOMDEL) and heterozygous
loss (HETLOSS). Lesions larger than 8 cm were four times more likely to have no
CDKN2A alteration (OR: 4.3; 95% CI:1.5-12.1; p<0.001). We developed a novel
integrated PACS informatics platform for the assessment of GBM molecular
subtypes and show that tumors with HOMDEL are more likely to have radiographic
evidence of pial invasion and less likely to have deep white matter invasion or
subependymal invasion. These imaging features may allow noninvasive
identification of CDKN2A allele status.
