A comprehensive survey on the use of deep learning techniques in glioblastoma.

in Artificial intelligence in medicine by Ichraq El Hachimy, Douae Kabelma, Chaimae Echcharef, Mohamed Hassani, Nabil Benamar, Nabil Hajji

TLDR

  • The study explores the use of artificial intelligence (AI) in the treatment of glioblastoma, a type of brain tumor. The study found that AI can help doctors better understand the tumor and make better treatment decisions. However, the study also found that glioblastoma is a complex and diverse disease, which makes it difficult to treat. The study suggests that future research should focus on using AI to better understand the genetic and molecular changes that occur in glioblastoma, as well as developing new treatments that target these changes.

Abstract

Glioblastoma, characterized as a grade 4 astrocytoma, stands out as the most aggressive brain tumor, often leading to dire outcomes. The challenge of treating glioblastoma is exacerbated by the convergence of genetic mutations and disruptions in gene expression, driven by alterations in epigenetic mechanisms. The integration of artificial intelligence, inclusive of machine learning algorithms, has emerged as an indispensable asset in medical analyses. AI is becoming a necessary tool in medicine and beyond. Current research on Glioblastoma predominantly revolves around non-omics data modalities, prominently including magnetic resonance imaging, computed tomography, and positron emission tomography. Nonetheless, the assimilation of omic data-encompassing gene expression through transcriptomics and epigenomics-offers pivotal insights into patients' conditions. These insights, reciprocally, hold significant value in refining diagnoses, guiding decision- making processes, and devising efficacious treatment strategies. This survey's core objective encompasses a comprehensive exploration of noteworthy applications of machine learning methodologies in the domain of glioblastoma, alongside closely associated research pursuits. The study accentuates the deployment of artificial intelligence techniques for both non-omics and omics data, encompassing a range of tasks. Furthermore, the survey underscores the intricate challenges posed by the inherent heterogeneity of Glioblastoma, delving into strategies aimed at addressing its multifaceted nature.

Overview

  • The study focuses on the application of machine learning techniques in the domain of glioblastoma, a highly aggressive brain tumor characterized by genetic mutations and disruptions in gene expression. The study aims to explore the deployment of AI for both non-omics and omics data, encompassing a range of tasks, and address the challenges posed by the inherent heterogeneity of glioblastoma. The study's primary objective is to provide a comprehensive overview of the notable applications of machine learning methodologies in glioblastoma research.

Comparative Analysis & Findings

  • The study does not provide a direct comparative analysis of outcomes under different experimental conditions or interventions. However, the study highlights the potential of machine learning techniques in refining diagnoses, guiding decision-making processes, and devising efficacious treatment strategies for glioblastoma. The study also underscores the challenges posed by the inherent heterogeneity of glioblastoma, which requires the development of strategies to address its multifaceted nature.

Implications and Future Directions

  • The study's findings suggest that machine learning techniques hold significant value in the domain of glioblastoma research. The study underscores the need for the development of strategies to address the challenges posed by the inherent heterogeneity of glioblastoma. Future research should focus on the integration of omic data-encompassing gene expression through transcriptomics and epigenomics, as well as the development of novel machine learning techniques tailored to the specific needs of glioblastoma research. The study also highlights the importance of addressing the ethical implications of AI in medicine, particularly in the context of patient privacy and data security.
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