Toolkit for Determination of Dose-response Relations, Validation of Radiobiological Parameters and Treatment Plan Optimization Based on Radiobiological Measures

Abstract

Accurately determined dose-response relations of the different tumors and normal tissues should be estimated and used in the clinic. The aim of this study is to demonstrate developed tools that are necessary for determining the dose-response parameters of tumors and normal tissues, for clinically verifying already published parameter sets using local patient materials and for making use of all this information in the optimization and comparison of different treatment plans and radiation techniques. The presented toolkit appears to be very convenient and efficient for clinical implementation of radiobiological modeling. It can also be used for the development of a clinical data and health information database for assisting the performance of epidemiological studies and the collaboration between different institutions within research and clinical frameworks.

Improved tumour response prediction with equivalent uniform dose in pre-clinical study using direct intratumoural infusion of liposome-encapsulated186Re radionuclides

“…The exact radiobiological parameters of the tumour model used in this work were unknown, as is commonly seen in a clinical setting. Generally, standard published values for the type of tumour in question are used for radiobiological modelling; however, newer techniques using maximum-likelihood estimation for parameter determination and compatibility testing seems beneficial in building strong dose-response relationships for tumours and normal tissues in a clinical setting (Mavroidis et al 2010). This work assessed EUD sensitivity to changes in each radiobiological parameter similar to previous work by Ebert (2000).…”

Prostate cancer tumour control probability modelling for external beam radiotherapy based on multi-parametric MRI-GTV definition

“…For the optimization we used simulated annealing (SA), a stochastic solver as implemented in open source "Object Oriented Optimization Toolbox" .NET library [21]. The estimation of the confidence interval (CI) for the parameter values was based on the likelihood profiling method, without assuming normality of the maximum likelihood estimator [22].…”

Prostate cancer tumour control probability modelling for external beam radiotherapy based on multi-parametric MRI-GTV definition

“…For the optimization we used simulated annealing (SA), a stochastic solver as implemented in open source "Object Oriented Optimization Toolbox" .NET library [17] . The estimation of the confidence interval (CI) for the parameter values was based on the likelihood profiling method, without assuming normality of the maximum likelihood estimator [18].…”