A functional artificial neural network for noninvasive presurgical evaluation of glioblastoma multiforme prognosis and radiosensitivity profiling

Background and Purpose Genetic profiling for glioblastoma multiforme (GBM) patients with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis mainly due to the functional involvement of CRL2 in mediating hypoxia-inducible factor 1 (HIF-1) α and epidermal growth factor receptor (EGFR) degradation. Because CUL2 expression levels are closely regulated with its copy number variations (CNVs), this study aims to develop an artificial neural network (ANN) that can predict GBM prognosis and help optimize personalized GBM treatment planning. Materials and Methods Datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM), the Chinese Glioma Genome Atlas (CGGA) were analyzed. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. Results We developed a 4-layer neural network that can consistently predict GBM prognosis with 80-85% accuracy with 3 inputs including CUL2 copy number, patient’s age at GBM diagnosis, and surface vs. volume (SvV) ratio. Conclusion A functional 4-layer neural network was constructed that can predict GBM prognosis and potential radiosensitivity.