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In this paper, we examine the effect of treatment parameters in a model used to evaluate permanent prostate implants. The model considers the prostate to be composed of 12 sub-sections, each sub-section is assigned a cell density based on the probability of finding cancer foci in that sub-section. Wasted dose as a result of the dose rate from the implant falling below a level adequate to counteract repopulation was found to vary by 2-16% over the range of radiosensitivity and repopulation rates considered. Within the model, applied to five dose distributions, the uncertainty in the tumour control probability (TCP) values calculated for each sub-section as a result of differences in the model parameters, was found to be less than 12% in most cases for the good quality implants. The difference in TCP values was much larger for the poor quality implant. Substituting a heterogeneous distribution of alpha for a single mean value resulted in generally lower TCP values though introducing a cutoff value with a Gaussian distribution had a profound effect on the calculated values. Despite uncertainties in the parameters, the model was able to identify sub-sections at risk of local recurrence but as a result of these uncertainties, the TCP values can only be considered in the relative rather than absolute sense.

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