Santo, Irene Dixe de Oliveira

Abstract BACKGROUND 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. 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 withVisually AcceSAble Rembrandtr Images (VASARI) for large datasets in PACS. MATERIAL AND METHODS 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 algorithmsincorporated within PACS. VASARI features were assessed using FHIR forms integrated within PACS. RESULTS 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). The pial invasion was predictive of HOMDEL (OR: 8.1, 95% CI: 1.8-53.2; p<0.012) as tumors with pial invasion were eight times more likely to be HOMDEL, even after adjusting for deep white matter and subependymal invasion. CONCLUSION 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.