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Analysis of Radiation Pneumonitis Risk Using a Generalized Lyman Model

Journal Article · · International Journal of Radiation Oncology, Biology and Physics
 [1]; ;  [2];  [3];  [4];  [2]; ;  [1]
  1. Department of Radiation Physics, University of Texas M.D. Anderson Cancer Center, Houston, Texas (United States)
  2. Department of Radiation Oncology, University of Texas M.D. Anderson Cancer Center, Houston, Texas (United States)
  3. Department of Radiation Oncology, Cancer Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing (China)
  4. Department of Radiation Oncology, Hunan Tumor Hospital and Institution, Changsha, Hunan (China)

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.

OSTI ID:
21124486
Journal Information:
International Journal of Radiation Oncology, Biology and Physics, Journal Name: International Journal of Radiation Oncology, Biology and Physics Journal Issue: 2 Vol. 72; ISSN IOBPD3; ISSN 0360-3016
Country of Publication:
United States
Language:
English

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