A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients. Issue 1 (18th November 2021)

• Record Type: Journal Article
• Authors: Zander, Eric; Ardeleanu, Andrew; Singleton, Ryan; Bede, Barnabas; Wu, Yilin; Zheng, Shuhua
• Is Part Of: Neuro-oncology advances. Volume 4:Issue 1(2022)
• Publication Date: 2021-11-18
• Subjects: artificial intelligence -- copy number variations -- cullin2 -- glioblastoma -- machine learning; 616.99481
• DOI: 10.1093/noajnl/vdab167
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Background: Pretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling 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. CUL2 expression levels are closely regulated with its copy number variations (CNVs). This study aims to develop artificial neural networks (ANNs) for pretreatment evaluation of GBM patients with inputs obtainable without intracranial surgical biopsies. Methods: Public datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the Chinese Glioma Genome Atlas (CGGA) were used for training and testing of the ANNs. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. A ratio comparing the surface area of tumor borders versus the total volume (SvV) was derived from the DICOM-SEG conversions of segmented tumors. The edges of these borders were detected using the canny edge detector. Packages including Keras, Pytorch, and TensorFlow were tested to build the ANNs. A 4-layered ANN (8-8-8-2) with a binary output was built with optimal performance after extensive testing. Results: The 4-layered deep learning ANN can identify a GBM patient's overall survival (OS) cohort with 80%–85% accuracy. The ANN requires 4 inputs, including CUL2 copy number, patients' age at GBM diagnosis, Karnofsky Performance Scale (KPS), and SvV ratio. Conclusion: Quantifiable image features can significantly improve the ability of ANNs to identify a GBM patients' survival cohort. Features such as clinical measures, genetic data, and image data, can be integrated into a single ANN for GBM pretreatment evaluation.