Prediction of Lymph Node Metastasis in Non-Small Cell Lung Carcinoma Using Primary Tumor Somatic Mutation Data.

Journal: JCO clinical cancer informatics
Published Date: May 30, 2025

Abstract

PURPOSE: Lymph node metastasis (LNM) significantly affects prognosis and treatment strategies in non-small cell lung cancer (NSCLC). Current diagnostic methods, including imaging and histopathology, have limited sensitivity and specificity. This study aims to develop and evaluate machine learning (ML) models that predict LNM in NSCLC using single-nucleotide polymorphism (SNP) data from The Cancer Genome Atlas.

Authors

  • Victor Lee
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Nicholas S Moore
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Joshua Doyle
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Daniel Hicks
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Patrick Oh
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Shari Bodofsky
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Sajid Hossain
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Abhijit A Patel
    Department of Therapeutic Radiology, Yale School of Medicine, New Haven, CT.
  • Sanjay Aneja
    Yale University, New Haven, Connecticut.
  • Robert Homer
    Department of Pathology, Yale School of Medicine, New Haven, CT.
  • Henry S Park
    Department of Therapeutic Radiology, Yale University School of Medicine, New Haven, Connecticut.

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