Preprint Brief Report Version 1 Preserved in Portico This version is not peer-reviewed

Glioma Image Analysis to Accurately Classify MGMT and Predict Drug Effectiveness

Version 1 : Received: 13 December 2020 / Approved: 15 December 2020 / Online: 15 December 2020 (13:12:35 CET)

How to cite: Mehta, V. Glioma Image Analysis to Accurately Classify MGMT and Predict Drug Effectiveness. Preprints 2020, 2020120386 (doi: 10.20944/preprints202012.0386.v1). Mehta, V. Glioma Image Analysis to Accurately Classify MGMT and Predict Drug Effectiveness. Preprints 2020, 2020120386 (doi: 10.20944/preprints202012.0386.v1).

Abstract

Glioblastoma multiforme is a deadly brain cancer with a median patient survival time of 18-24 months. A single biopsy cannot provide complete assessment of the tumor’s microenvironment, making personalized care limited. 50% of the patients do not respond to the anti-cancer drug Temozolomide(TMZ) because of the over-expression of MGMT gene. Epigenetic silencing of the MGMT gene by methylation results in decreased MGMT expression, resulting in increased sensitivity to TMZ, and longer survival. The purpose of this research is to use artificial intelligence (AI) to design a low cost methodology to determine the MGMT’s methylation status and suggest non-invasive treatment plan.

Subject Areas

glioblastoma; cancer; genetics; deep learning; MGMT

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