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An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Are more complicated tumour control probability models better?

Jiafen Gong¹, Mairon M Dos Santos, Chris Finlay

¹Centre for Mathematical Biology, Department of Mathematical & Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada.

Mathematical Medicine and Biology : a Journal of the IMA

|October 19, 2011

Summary

The Poissonian tumour control probability (TCP) model is recommended for routine cancer radiation treatment planning. Complex models offer similar predictions in most scenarios, but require careful parameter selection.

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Area of Science:

Radiation oncology
Mathematical modeling
Cancer research

Background:

Tumour control probability (TCP) models are crucial for predicting radiation therapy success.
Existing models range from simple Poissonian approaches to complex birth-death processes.
The choice of TCP model can impact treatment planning and outcomes.

Purpose of the Study:

To systematically compare six different TCP models, including Poissonian, Zaider-Minerbo, and Monte Carlo, along with their cell cycle variants.
To evaluate these models using realistic prostate cancer treatment protocols (fractionated radiotherapy, brachytherapy).
To determine the practical differences and applicability of various TCP models in clinical settings.

Main Methods:

Comparison of six distinct tumour control probability (TCP) models.
Evaluation using clinical non-uniform treatment protocols for prostate cancer.
Analysis of fractionated external beam radiotherapy and high/low dose rate brachytherapies.

Main Results:

One-compartment and two-compartment TCP models yield similar predictions in most realistic scenarios.
Differences emerge due to the radiosensitivity of quiescent cells in two-compartment models.
A unified 'effective' hazard function was developed to combine different treatment approaches.

Conclusions:

The Poissonian TCP model is suitable for everyday radiation treatment planning.
More complex models are generally not necessary unless specific conditions warrant their use.
Careful selection of model parameters (radiosensitivity, hazard function) is essential for accurate predictions.