Can dose convolution modelling explain bath and shower effects in rat spinal cord?

Objective. 'Bath and shower' effects were first seen in proton irradiations of rat spinal cord, where a low dose 'bath' reduced the smaller field 'shower' dose needed for limb paralysis giving the appearance of sensitisation of the cord or disproportionate response. This was difficult to reconcile with existing tissue complication models. The purpose of this investigation is to explore a different approach using a dose convolution algorithm to model the 50% isoeffect endpoint.Approach.Bath and shower dose distributions were convolved with Gaussian functions with widths specified by theσparameter. The hypothesis was that the maximum value from the convolved distributions was constant for isoeffect across the modelled scenarios. A simpler field length dependent relative biological effectiveness (FLRBE) approach was also used for a subset of the data which gave results independent ofσ.Main results.The maximum values from the convolved distributions were constant within ±17% across the bath and shower experiments forσ = 3.5 mm, whereas the maximum dose varied by a factor of four. The FLRBE results were also within ±14% confirming the validity of the dose convolution approach.Significance.A simple approach using dose convolution modelling of the 50% isotoxicity gave compelling consistency with the full range of bath and shower results, while the FLRBE approach confirmed the results for the symmetric field data. Convolution modelling and the effect of time interval were consistent with a signalling factor diffusion mechanism such as the 'bystander effect'. The results suggest biological effectiveness is reduced for very small field sizes, requiring a higher isoeffect dose. By implication, the bath dose does notsensitisethe cord to the shower dose; when biological effectiveness is accounted for, a small increase in the bath dose requires a significantly larger reduction in shower dose for isoeffect.

A study of the clinical, treatment planning and dosimetric feasibility of dose painting in external beam radiotherapy of prostate cancer.

BACKGROUND AND PURPOSE: Radiotherapy dose painting is a promising technique which enables dose escalation to areas of higher tumour cell density within the prostate which are associated with radioresistance, known as dominant intraprostatic lesions (DILs). The aim of this study was to determine factors affecting the feasibility of radiotherapy dose painting in patients with high and intermediate risk prostate cancer. MATERIALS & METHODS: Twenty patients were recruited into the study for imaging using a 3 T magnetic resonance imaging (MRI) scanner. Identified DILs were outlined and the scan registered with the planning computed tomography (CT) dataset. Intensity-modulated plans were produced and evaluated to determine the effect of the organ-at-risk constraints on the dose that could be delivered to the DILs. Measurements were made to verify that the distribution could be safely delivered. RESULTS: MRI scans were obtained for nineteen patients. Fourteen patients had one to two DILs with ten overlapping the urethra and/or rectum. The target boost of 86 Gy was achieved in seven plans but was limited to 80 Gy for five patients whose boost volume overlapped or abutted the urethra. Dosimetric measurements gave a satisfactory gamma pass rate at 3%/3 mm. CONCLUSIONS: It was feasible to produce dose-painted plans for a boost of 86 Gy for approximately half the patients with DILs. The main limiting factor was the proximity of the urethra to the boost volumes. For a small proportion of patients, rigid registration between CT and MRI images was not adequate for planning purposes.

Artificial neural network modelling of megavoltage photon dose distributions.

An artificial neural network (NN) has been used to model the two-dimensional dose distributions from a Varian 2100C linac. The network was trained using depth dose data for 6 and 10 MV x-rays, collected during the linac commissioning phase. During training, the number of iterations and hidden nodes was adjusted manually until acceptable agreement between measured and predicted data was obtained. In order to validate the network a subset of the data was set aside and not used for training. This enabled the performance of the network to be investigated in terms of generalization and accuracy, together with its ability to interpolate between different field sizes and positions in the beam. Finally, the network was used to generate data points over a 2D grid so that isodose distributions could be visualized. Good agreement was found between measured data and that produced by the trained neural network.