Machine Learning Enabled Prediction of Biologically Relevant Gene Expression Using CT-Based Radiomic Features in Non-Small Cell Lung Cancer.

Journal: Cancer medicine
PMID: 39718015

Abstract

BACKGROUND: Non-small-cell lung cancer (NSCLC) remains a global health challenge, driving morbidity and mortality. The emerging field of radiogenomics utilizes statistical methods to correlate radiographic tumor features with genomic characteristics from biopsy samples. Radiomic techniques automate the precise extraction of imaging features from tumor regions in radiographic scans, which are subjected to machine learning (ML) to predict genomic attributes.

Authors

  • Shrey S Sukhadia
    Centre for Genomics and Personalized Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.
  • Christoph Sadee
    Stanford Center for Biomedical Informatics Research, Department of Medicine and Biomedical Data Science, Stanford University, California, USA.
  • Olivier Gevaert
    Department of Biomedical Data Science, Stanford University, CA, 94305, USA.
  • Shivashankar H Nagaraj
    Centre for Genomics and Personalized Health and School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia.

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