Impact of radiobiological parameters on tumor control probability in rectal cancer using the Poisson linear quadratic model

  • Duong Thanh Tai
  • , Nguyen Ngoc Anh*
  • , Luong Tien Phat
  • , Tran Trung Kien
  • , Nguyen Xuan Hai
  • , Nguyen Huu Tiep*
  • , Peter Sandwall
  • , Hiba Omer
  • , Abdelmoneim Sulieman
  • , Nissren Tamam
  • , James C.L. Chow*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

We explore several radiobiological parameters, specifically the tissue-specific linear quadratic α/β, the maximum normalized gradient of dose response γ, and the dose giving a 50 % response probability for the tissue considered D50, looking at how these affect the computed Tumor Control Probabilities (TCPs) within the Poisson Linear Quadratic (LQ) model. A total of 3,584,000 TCP values were derived for 28 rectal cancer patients, utilizing two slightly different treatment plans: the short plan (50 Gy in 25 fractions) and the long plan (50.4 Gy in 28 fractions), along with 64,000 parameter combinations. TCP was calculated using the Poisson LQ model, which combines radiobiological parameters (α/β, γ, and D50) with the voxel-based Equivalent Dose (EQD). Our analysis reveals that D50 has the most significant influence, followed by γ, while α/β has minimal impact. Moreover, the influence of γ depends on the specific D50 value employed. Furthermore, comparing TCPs obtained for short and long plans, these consistently show the TCP of the short plan to surpass that of the long plan when the same radiobiological parameter values are used. This study suggests that evaluating treatment plan efficacy through TCPs it is appropriate to use consistent parameter values within the Poisson Linear Quadratic model.

Original languageEnglish
Article number112902
JournalRadiation Physics and Chemistry
Volume236
DOIs
StatePublished - Nov 2025

Keywords

  • Poisson linear quadratic model
  • Radiobiological parameter
  • Radiosensitivity
  • Rectal cancer
  • Tumor control probability
  • Tumour response
  • α/β ratio

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