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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Clinically Interpretable Survival Risk Stratification in Head and Neck Cancer Using Bayesian Networks and Markov

Keyur D Shah1, Ibrahim Chamseddine2, Xiaohan Yuan3

  • 1Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, Georgia.

International Journal of Radiation Oncology, Biology, Physics
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This summary is machine-generated.

This study identifies key factors for head and neck cancer survival using Bayesian networks. The developed model accurately predicts patient risk, aiding personalized treatment decisions.

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Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Head and neck cancer (HNC) survival prediction is complex, requiring identification of key prognostic factors.
  • Traditional models may not fully capture the intricate dependencies between clinical, anatomical, and treatment variables in HNC.

Purpose of the Study:

  • To develop a clinically interpretable Bayesian network (BN) model for HNC survival.
  • To identify a parsimonious set of survival-relevant features using the Markov blanket (MB) of 2-year survival (SVy2).
  • To evaluate the prognostic and causal utility of the derived BN model.

Main Methods:

  • Utilized the RADCURE dataset (3346 HNC patients) treated with definitive (chemo)radiation.
  • Constructed a probabilistic BN to model variable dependencies; extracted the MB of SVy2.
  • Trained a logistic regression model using MB features, validated on a temporal split dataset (2174 train/820 test) with performance metrics including AUC, C-index, and Kaplan-Meier analysis.

Main Results:

  • Identified 6 key features in the MB of SVy2: ECOG performance status, T stage, HPV status, disease site, primary GTV, and treatment modality.
  • The BN model achieved an AUC of 0.65 and C-index of 0.78 on test data, significantly stratifying patients by risk (log-rank P < .01).
  • Strong performance observed in subgroups: HPV-negative, T4 stage, and large GTV cohorts, with significant survival stratification.

Conclusions:

  • A compact, MB-derived BN model effectively stratifies survival risk in head and neck cancer.
  • The model's interpretable structure supports explainable prognostication and aids in individualized treatment decision-making.
  • Causal analysis indicated positive survival impact for ECOG 0, HPV-positive status, and chemoradiation.