Application of Novel PACS-based Informatics Platform to Identify Imaging Based Predictors of CDKN2A Allelic Status in Glioblastomas

TL;DR

This study introduces a new PACS-based informatics platform that uses deep learning and VASARI features to identify imaging biomarkers associated with CDKN2A allelic status in glioblastomas, potentially enabling noninvasive diagnosis.

Contribution

The paper presents a novel integrated PACS platform combining deep learning and VASARI features for rapid, large-scale analysis of glioblastoma imaging biomarkers related to CDKN2A mutations.

Findings

Tumors without CDKN2A alterations are larger than those with deletions.

Lesions over 8 cm are more likely to lack CDKN2A alterations.

HOMDEL tumors show specific invasive imaging features.

Abstract

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.