Multiomic based Bayesian network toxicity modeling for simultaneous prediction of multiple toxicity outcomes in NSCLC.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Published Date: Jul 1, 2025

Abstract

PURPOSE: Radiation Pneumonitis (RP) and Radiation Esophagitis (RE) are two prominent dose limiting toxicities from NSCLC radiotherapy. This study aimed to develop a multi-objective Bayesian network (BN) model to predict multiple NSCLC outcomes simultaneously using dose-volume histograms (DVH), clinical, and multiomic features.

Authors

  • Saurabh S Nair
    Departments of Radiation Physics and Thoaracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: ssnair@mdanderson.org.
  • Ramon M Salazar
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America.
  • Ting Xu
    Bioresources Green Transformation Collaborative Innovation Center of Hubei Province, College of Life Sciences, Hubei University, Wuhan 430062, Hubei, China.
  • Alexandra O Leone
    Departments of Radiation Physics and Thoaracic Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Zhongxing Liao
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Laurence E Court
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Joshua S Niedzielski
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX 77030, United States.

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