Impact of dose-distribution uncertainties on rectal ntcp modeling. II: Uncertainty implications

A trial of nonescalated conformal versus conventional radiotherapy treatment of prostate cancer has been carried out at the Royal Marsden NHS Trust (RMH) and Institute of Cancer Research (ICR), demonstrating a significant reduction in the rate of rectal bleeding reported for patients treated using the conformal technique. The rate of bleeding has been shown to fall significantly as the extent of rectal wall receiving a planned dose-level in excess of 57 Gy is reduced. Dose-distributions delivered to the rectal wall over the course of radiotherapy treatment inevitably differ from planned distributions. In a previous paper estimates were obtained of the uncertainties in some planned rectal dose-distribution parameters generated by patient setup error, rectal wall movement and the variable degree of rectal wall distension. Here these uncertainties are combined to obtain estimates of the total planning uncertainties in rectal dose-distribution parameters thought likely, on the basis of mechanistic biological modeling, to correlate strongly with the complication rate. Working from these totaled uncertainty values, together with values of patient-to-patient and technique-to-technique differences in planned dose-distribution parameters, it can be inferred that the rectal dose-distribution uncertainties: (i) Have only a marginal impact on fits of a normal tissue complication probability (ntcp) model to RMH/ICR dose-distribution and grade 1, 2, 3 bleeding data (slightly flattening observed volume-response curves); (ii) only slightly reduce the power of a 2 x 100 patient trial of conformal versus conventional prostate radiotherapy to detect a significantly lower rate of grade 1,2,3 rectal bleeding amongst conformally treated patients; (iii) do not diminish the information content of individual planned patient dose-distribution data to the point where the fitting of technique-averaged data would provide as sensitive a test of the existence of a volume effect as the fitting of individual patient data.