Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

Journal: International journal of radiation oncology, biology, physics
PMID: 38462018

Abstract

PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of data and provide a clinically reliable model for ORN risk evaluation through an unsupervised-learning analysis that incorporates the whole radiation dose distribution on the mandible.

Authors

  • Seyedmohammadhossein Hosseinian
    Department of Mechanical and Materials Engineering, University of Cincinnati, Cincinnati, Ohio. Electronic address: s.hosseinian@uc.edu.
  • Mehdi Hemmati
    School of Industrial and Systems Engineering, University of Oklahoma, Norman, Oklahoma.
  • Cem Dede
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Travis C Salzillo
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Lisanne V van Dijk
    Department of Radiation Oncology, University of Groningen, University Medical Center Groningen, The Netherlands. Electronic address: l.v.van.dijk@umcg.nl.
  • Abdallah S R Mohamed
    Anderson Cancer Center.
  • Stephen Y Lai
    Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, Texas.
  • Andrew J Schaefer
    Department of Computational Applied Mathematics & Operations Research, Rice University, Houston, Texas.
  • Clifton D Fuller
    Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.

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