Personalized Composite Dosimetric Score-Based Machine Learning Model of Severe Radiation-Induced Lymphopenia Among Patients With Esophageal Cancer.

Journal: International journal of radiation oncology, biology, physics
PMID: 38797500

Abstract

PURPOSE: Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetric parameters. However, dosimetric parameters, e.g. dose-volume (DV) indices, are highly correlated with one another and are only weakly associated with RIL. Here we introduce the novel concept of "composite dosimetric score" (CDS) as the index that condenses the dose distribution in immune tissues of interest to study the dosimetric dependence of RIL. We derived an improved multivariate classification scheme for risk of grade 4 RIL (G4RIL), based on this novel RT dosimetric feature, for patients receiving chemo RT for esophageal cancer.

Authors

  • Yan Chu
    McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Cong Zhu
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States of America. Department of Epidemiology, Human Genetics, and Environmental Sciences, The University of Texas Health Science Center at Houston, Houston, TX, United States of America. Authors to whom any correspondence should be addressed.
  • Brian P Hobbs
    Department of Quantitative Health Sciences and.
  • Yiqing Chen
    Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas; Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, Texas.
  • Peter S N van Rossum
    Department of Radiation Oncology, Amsterdam University Medical Center, Amsterdam, The Netherlands.
  • Clemens Grassberger
    Department of Radiation Oncology, University of Washington, Seattle, Washington.
  • Degui Zhi
    School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX, USA.
  • Steven H Lin
  • Radhe Mohan

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