Purpose: Although it is known that brachytherapy dose distributions are highly heterogeneous, the effect of particular dose distribution patterns on tumor control probability (TCP) is unknown. It is unlikely that clinical results will throw light on the question in the near future, given the long follow-up and detailed dosimetry required for each patient. We used detailed dose distribution data from 50 patients combined with radiobiologic parameters consistent with what is known about TCP curves for prostate cancer to study the changes in TCP that accompany gross dosimetric measures and particular dosing irregularities (e.g., moderate underdosing of large volumes vs. extreme underdosing of small volumes).
Methods and materials: For each of the 50 patients with organ-confined prostate cancer who had undergone 125I prostate implants alone at our clinic, postimplant CT scans were obtained approximately 1 month after implantation. Dose distribution information was obtained from postimplant dosimetry. The percentage of the prostate volume receiving a specified dose was recorded from the respective differential dose-volume histograms in 10-Gy bins. In addition, the percentage of prostate volume underdosed at varying fractions of the prescription dose were determined, as was the minimal prostate dose. The log-normal distributions of the radiobiologic parameters [ln(initial clonogen number), alpha, and alpha/beta] were adjusted so that the predicted population parameters (steepness and location) of the dose-response curves for external beam radiotherapy agreed with the published estimates. The variability in the dose-volume details was increased by scaling the dose distributions by factors ranging from 0.7 to 1.5, thereby simulating, for each of the patients, nine new patients with different total doses but identical relative distributions of the dose over the voxels. Radiobiologic variability between the selected dose distributions was then removed by averaging >50 randomly chosen sets of radiobiologic parameters from the log-normal distributions to estimate the TCP for each of the dose distributions, giving some insight into the TCP variations with conventional dosimetric indexes and different patterns of underdosing.
Results: Using the 450 dose distributions created by expanding the 50-patient data set, the volume of the prostate that was extremely underdosed (between 50% and 70% of the prescription dose) was related to the volume that was moderately underdosed (between 80% and 100% of the prescription dose). We found that the individual TCP is greatly dependent on the inhomogeneous dose distribution and the dosimetric indexes, such as the volume of prostate receiving 100% of the prescribed dose (V100) and the maximal dose received by 90% of the prostate volume (D90), which, by themselves, are not always accurate predictors of control probabilities. In a multivariate analysis of the dependence of TCP on these parameters (V100, D90, minimal dose, and moderately and severely underdosed volumes), only D90 and the minimal dose were statistically significant. Generally speaking, however, a lower minimal dose means a lower TCP.
Conclusion: The work described here was an hypothesis-generating study. Our results showed that even if the V100 and D90 are nearly identical for 2 patients, there can be (and frequently are) significant differences in the dose distributions in the subvolumes of the prostate. Under simulated dose-response conditions (i.e., with variations in the dose distribution), the D90 and minimal dose significantly affected the TCP but the V100 and the volumes moderately or severely underdosed did not. In general, one must consider the totality of the dose distribution to evaluate the dosimetric quality of a low-dose-rate prostate implant. TCP is not a monotonic function of extreme or moderate underdosing. In some instances, extreme underdosing of relatively small volumes may result in a greater TCP than moderate underdosing of relatively large volumes and vice versa.