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Poisson Linear-Quadratic Tumor Control Probability Model Validation in Micro- and Macroscopic Breast Cancer Radiation

Sergejs Unterkirhers1, Sara Erni2, Günther Gruber3

  • 1Radiation Oncology Department, Klinik Hirslanden, Zurich, Switzerland; Faculty of Science, University of Zurich, Zurich, Switzerland.

International Journal of Radiation Oncology, Biology, Physics
|November 25, 2025
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Summary
This summary is machine-generated.

A new tumor control probability model accurately predicts breast cancer radiation therapy outcomes by accounting for tumor size, patient radiosensitivity, and treatment time. This validated model can help personalize radiation doses for improved local control.

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Area of Science:

  • Radiation Oncology
  • Mathematical Modeling
  • Breast Cancer Research

Background:

  • Accurate prediction of local control (LC) in breast cancer radiation therapy (RT) is crucial for optimizing treatment strategies.
  • Existing models often do not fully account for tumor heterogeneity, interpatient variability, and treatment time effects.

Purpose of the Study:

  • To validate a mechanistic Poissonian linear-quadratic tumor control probability (TCP) model.
  • The model incorporates tumor volume heterogeneity, interpatient radiosensitivity variability, and treatment time effects.
  • Validation was performed using published breast cancer RT data sets.

Main Methods:

  • Local control data were obtained from the START A/B, FAST-Forward, and Early Breast Cancer Trialists' Collaborative Group studies.
  • A Poissonian linear-quadratic model was fitted to observed local control rates, incorporating parameters for interpatient heterogeneity, tumor volume scaling, and time-to-failure.
  • The model quantified the effect of overall treatment duration on locoregional recurrence and predicted in-breast recurrence rates for various tumor sizes.

Main Results:

  • The model successfully reproduced 5-year local control outcomes in both adjuvant and RT-alone settings.
  • The estimated residual clonogen count after surgery was 125, and the effect of overall treatment time was 0.58 Gy/d.
  • Simulations demonstrated a strong tumor-size dependence, with smaller tumors showing higher control rates across different fractionation schemes; larger tumors (>4 cm) had limited control with evaluated regimens.

Conclusions:

  • The validated Poissonian linear-quadratic TCP model provides a robust framework for predicting local control in breast cancer RT.
  • Incorporating tumor size heterogeneity, radiosensitivity variability, and time effects enhances predictive accuracy.
  • Further validation could establish this model as a valuable tool for tailoring RT dose regimens to individual patient and tumor characteristics.