title: Are more complicated tumour control probability models better?
abstract: Mathematical models for the tumour control probability (TCP) are used to estimate the expected success of radiation treatment protocols of cancer. There are several TCP models in the literature, from the simplest (Poissonian TCP) to the well-advanced stochastic birth-death processes. Simple and complex models often make the same predictions. Hence, here, we present a systematic study where we compare six of these TCP models: the Poisson TCP, the Zaider-Minerbo TCP, a Monte Carlo TCP and their corresponding cell cycle (two-compartment) models. Several clinical non-uniform treatment protocols for prostate cancer are employed to evaluate these models. These include fractionated external beam radiotherapies, and high and low dose rate brachytherapies. We find that in realistic treatment scenarios, all one-compartment models and all two-compartment models give basically the same results. A difference occurs between one-compartment and two-compartment models due to reduced radiosensitivity of quiescent cells.We find that care must be taken for the right choice of parameters, such as the radiosensitivities α and β and the hazard function h. Typically, different hazard functions are used for fractionated treatment (fractionated survival fraction) and for brachytherapies (Lea-Catcheside protraction factor). We were able to combine these two approaches into one 'effective' hazard function. Based on our results, we can recommend the use of the Poissonian TCP for everyday treatment planning. More complicated models should only be used when absolutely necessary.
authors: Centre for Mathematical Biology, Department of Mathematical & Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada.
authors: Jiafen
authors: J
authors: Gong
authors: Mairon M
authors: MM
authors: Dos Santos
authors: Chris
authors: C
authors: Finlay
authors: Thomas
authors: T
authors: Hillen
mesh_terms: D001918:Brachytherapy; D002453:Cell Cycle / Q000528:radiation effects; D019583:Dose Fractionation, Radiation; D006801:Humans; D008297:Male; D055641:Mathematical Concepts; D008954:Models, Biological; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000473:pathology / Q000532:radiotherapy*; D016012:Poisson Distribution; D011471:Prostatic Neoplasms / Q000473:pathology / Q000532:radiotherapy; D011836:Radiation Tolerance; D011880:Radiotherapy Planning, Computer-Assisted / Q000706:statistics & numerical data; D013269:Stochastic Processes