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Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.

Biswajit Jena1, Sanjay Saxena1, Gopal Krishna Nayak1

  • 1Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India.

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|August 26, 2022
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Summary
This summary is machine-generated.

Brain tumor characterization leverages radiomics and radiogenomics within artificial intelligence (AI) for personalized medicine. This AI-driven approach enhances understanding of tumor genetics and improves treatment outcomes.

Keywords:
brain tumorbrain tumor characterizationclassificationgenomicsradiogenomicsradiomicsrisk-of-biassegmentation

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Area of Science:

  • Oncology
  • Radiology
  • Genomics
  • Artificial Intelligence

Background:

  • Brain tumor characterization (BTC) involves analyzing tumor causes and traits via segmentation, classification, detection, and risk assessment.
  • Radiomics extracts quantitative features from radiological images using artificial intelligence (AI).
  • Radiogenomics integrates radiomics and genomics for advanced disease characterization, including genetic information and mutation status.

Purpose of the Study:

  • To review brain tumor characterization using radiomics and radiogenomics within an AI environment.
  • To analyze the statistical observations and risk-of-bias (RoB) in AI-driven radiogenomics for brain tumors.
  • To highlight the benefits of AI in radiogenomics for personalized treatment and individualized medicine.

Main Methods:

  • A systematic literature search using the PRISMA approach was conducted across databases including IEEE, Google Scholar, PubMed, MDPI, and Scopus.
  • 121 relevant studies were identified for the review.
  • Statistical observation and risk-of-bias (RoB) analysis were employed to evaluate the findings.

Main Results:

  • Radiomics and radiogenomics have demonstrated significant success and advantages in various oncology applications.
  • AI, particularly deep learning, has positively impacted radiogenomics outcomes for brain tumor characterization.
  • Risk-of-bias analysis provides insights into AI architectures and their associated biases in radiogenomics.

Conclusions:

  • AI-powered radiomics and radiogenomics are effective tools for brain tumor characterization.
  • The integration of AI enhances personalized treatment strategies and individualized medicine in oncology.
  • Further understanding of AI biases is crucial for optimizing radiogenomics approaches.