Analysis of Radiation Pneumonitis Risk Using a Generalized Lyman Model

Susan L. Tucker, H. Helen Liu, Zhongxing Liao, Xiong Wei, Shulian Wang, Hekun Jin, Ritsuko Komaki, Mary K. Martel, Radhe Mohan

Research output: Contribution to journalArticlepeer-review

78 Scopus citations

Abstract

Purpose: To introduce a version of the Lyman normal-tissue complication probability (NTCP) model adapted to incorporate censored time-to-toxicity data and clinical risk factors and to apply the generalized model to analysis of radiation pneumonitis (RP) risk. Methods and Materials: Medical records and radiation treatment plans were reviewed retrospectively for 576 patients with non-small cell lung cancer treated with radiotherapy. The time to severe (Grade ≥3) RP was computed, with event times censored at last follow-up for patients not experiencing this endpoint. The censored time-to-toxicity data were analyzed using the standard and generalized Lyman models with patient smoking status taken into account. Results: The generalized Lyman model with patient smoking status taken into account produced NTCP estimates up to 27 percentage points different from the model based on dose-volume factors alone. The generalized model also predicted that 8% of the expected cases of severe RP were unobserved because of censoring. The estimated volume parameter for lung was not significantly different from n = 1, corresponding to mean lung dose. Conclusions: NTCP models historically have been based solely on dose-volume effects and binary (yes/no) toxicity data. Our results demonstrate that inclusion of nondosimetric risk factors and censored time-to-event data can markedly affect outcome predictions made using NTCP models.

Original languageEnglish (US)
Pages (from-to)568-574
Number of pages7
JournalInternational Journal of Radiation Oncology Biology Physics
Volume72
Issue number2
DOIs
StatePublished - Oct 1 2008

Keywords

  • Lung cancer
  • Lyman model
  • NTCP
  • Radiation pneumonitis
  • Smoking

ASJC Scopus subject areas

  • Radiation
  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

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