Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma.

Journal: Journal of translational medicine
PMID: 39871351

Abstract

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor associated with a poor patient prognosis. The survival rate remains low despite standard therapies, highlighting the urgent need for novel treatment strategies. Advanced imaging techniques, particularly magnetic resonance imaging (MRI), are crucial in assessing GBM. Disruptions in various oncogenic signaling pathways, such as Receptor Tyrosine Kinase (RTK)-Ras-Extracellular signal-regulated kinase (ERK) signaling, Phosphoinositide 3- Kinases (PI3Ks), tumor protein p53 (TP53), and Neurogenic locus notch homolog protein (NOTCH), contribute to the development of different tumor types, each exhibiting distinct morphological and phenotypic features that can be observed at a microscopic level. However, identifying genetic abnormalities for targeted therapy often requires invasive procedures, prompting exploration into non-invasive approaches like radiogenomics. This study explores the utility of radiogenomics and machine learning (ML) in predicting these oncogenic signaling pathways in GBM patients.

Authors

  • Abdul Basit Ahanger
    Department of Computer Science, Islamic University of Science and Technology (IUST), Kashmir, 192122, India.
  • Syed Wajid Aalam
    Department of Computer Science, Islamic University of Science and Technology (IUST), Kashmir, 192122, India.
  • Tariq Ahmad Masoodi
    Human Immunology Department, Research Branch, Sidra Medicine, Doha, Qatar.
  • Asma Shah
    Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology (IUST), Kashmir, 192122, India.
  • Meraj Alam Khan
    Quantlase Lab LLC, Masdar City, Abu Dhabi, United Arab Emirates.
  • Ajaz A Bhat
    Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar.
  • Assif Assad
    Department of Computer Science and Engineering, Islamic University of Science and Techonology Kashmir, Awantipora, 192122, J&K, India. Electronic address: assif.assad@islamicuniversity.edu.in.
  • Muzafar Ahmad Macha
    Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology (IUST), Kashmir, 192122, India. muzafar.macha@iust.ac.in.
  • Muzafar Rasool Bhat
    Department of Computer Science, Islamic University of Science and Technology (IUST), Kashmir, 192122, India. muzafarrasool@gmail.com.

Keywords

External Resources

Popular Topics
Recent Journals