Risk and Quality in Brachytherapy From a Technical Perspective.

AIMS: To provide an overview of the history of incidents in brachytherapy and to describe the pillars in place to ensure that medical physicists deliver high-quality brachytherapy. MATERIALS AND METHODS: A review of the literature was carried out to identify reported incidents in brachytherapy, together with an evaluation of the structures and processes in place to ensure that medical physicists deliver high-quality brachytherapy. In particular, the role of education and training, the use of process and technical quality assurance and the role of international guidelines are discussed. RESULTS: There are many human factors in brachytherapy procedures that introduce additional risks into the process. Most of the reported incidents in the literature are related to human factors. Brachytherapy-related education and training initiatives are in place at the societal and departmental level for medical physicists. Additionally, medical physicists have developed process and technical quality assurance procedures, together with international guidelines and protocols. Education and training initiatives, together with quality assurance procedures and international guidelines may reduce the risk of human factors in brachytherapy. CONCLUSION: Through application of the three pillars (education and training; process control and technical quality assurance; international guidelines), medical physicists will continue to minimise risk and deliver high-quality brachytherapy treatments.

Mathematical optimization of high dose-rate brachytherapy-derivation of a linear penalty model from a dose-volume model.

High dose-rate brachytherapy is a method for cancer treatment where the radiation source is placed within the body, inside or close to a tumour. For dose planning, mathematical optimization techniques are being used in practice and the most common approach is to use a linear model which penalizes deviations from specified dose limits for the tumour and for nearby organs. This linear penalty model is easy to solve, but its weakness lies in the poor correlation of its objective value and the dose-volume objectives that are used clinically to evaluate dose distributions. Furthermore, the model contains parameters that have no clear clinical interpretation. Another approach for dose planning is to solve mixed-integer optimization models with explicit dose-volume constraints which include parameters that directly correspond to dose-volume objectives, and which are therefore tangible. The two mentioned models take the overall goals for dose planning into account in fundamentally different ways. We show that there is, however, a mathematical relationship between them by deriving a linear penalty model from a dose-volume model. This relationship has not been established before and improves the understanding of the linear penalty model. In particular, the parameters of the linear penalty model can be interpreted as dual variables in the dose-volume model.

Nanodosimetry and RBE values in radiotherapy.

In a recent paper, the authors reported that the dose mean lineal energy, [Formula: see text] in a volume of about 10-15 nm is approximately proportional to the α-parameter in the linear-quadratic relation used in fractionated radiotherapy in both low- and high-LET beams. This was concluded after analyses of reported radiation weighting factors, WisoE (clinical RBE values), and [Formula: see text] values in a large range of volumes. Usually, microdosimetry measurements in the nanometer range are difficult; therefore, model calculations become necessary. In this paper, the authors discuss the calculation method. A combination of condensed history Monte Carlo and track structure techniques for calculation of mean lineal energy values turned out to be quite useful. Briefly, the method consists in weighting the relative dose fractions of the primary and secondary charged particles with their respective energy-dependent dose mean lineal energies. The latter were obtained using a large database of Monte Carlo track structure calculations.

Lineal energy and radiation quality in radiation therapy: model calculations and comparison with experiment.

Microdosimetry is a recommended method for characterizing radiation quality in situations when the biological effectiveness under test is not well known. In such situations, the radiation beams are described by their lineal energy probability distributions. Results from radiobiological investigations in the beams are then used to establish response functions that relate the lineal energy to the relative biological effectiveness (RBE). In this paper we present the influence of the size of the simulated volume on the relation to the clinical RBE values (or weighting factors). A single event probability distribution of the lineal energy is approximated by its dose average lineal energy (y[overline](D)) which can be measured or calculated for volumes from a few micrometres down to a few nanometres. The clinical RBE values were approximated as the ratio of the α-values derived from the LQ-relation. Model calculations are presented and discussed for the SOBP of a (12)C ion (290 MeV u(-1)) and the reference (60)Co γ therapy beam. Results were compared with those for a conventional x-ray therapy beam, a 290 MeV proton beam and a neutron therapy beam. It is concluded that for a simulated volume of about 10 nm, the α-ratio increases approximately linearly with the y[overline](D)-ratio for all the investigated beams. The correlation between y and α provides the evidence to characterize a radiation therapy beam by the lineal energy when, for instance, weighting factors are to be estimated.