Salvage radiotherapy for prostate cancer: Finding a way forward using radiobiological modeling


Purpose: Recent modeling efforts, based on reported outcomes following salvage radiotherapy (SRT) for prostate cancer, predict the likelihood of biochemical control (tumor control probability, TCP) as a function of pre-treatment prostate specific antigen (PSA) and SRT dose. Similar instruments predict the risk of grade ≥ 3 late toxicity (normal tissue complication probability, NTCP) as a function of SRT dose. Here we explore how changes in the parameters of those models might affect the optimal SRT dose and clinical outcomes.

Materials and Methods: Baseline TCP and NTCP model parameters were established in a previous report. Pre-treatment PSA was set at 0.4 ng/mL. Model parameters were modified to explore four scenarios: (1) improving the safety of SRT, (2) increasing tumor cell radiosensitivity, (3) increasing the cure rate achievable with SRT and (4) adoption of hypofractionated SRT schedules. The “optimal” SRT dose, defined as the dose that maximized the likelihood of achieving biochemical control without causing late toxicity, was identified for each scenario.

Results: Improving the safety of SRT increased the optimal SRT dose, while radiosensitization decreased the optimal dose. Both changes were predicted to increase the probability of biochemical control and decrease late toxicity rates. Increasing the cure rate achievable with SRT (eg: improving patient selection or combining SRT with effective systemic therapy) provided the greatest gains in TCP. Adoption of a hypofractionated SRT schedule was predicted to improve both biochemical control and late toxicity.

Conclusions: Modeling exercises demonstrate the significant gains that may be achieved with improved implementation of SRT for prostate cancer. Strategies to realize the effects modeled in this report should be explored in clinical trials.

Full Text Options
 Full Text
1449 - 1453
Research Paper
 Cite This Article
Salvage radiotherapy for prostate cancer: Finding a way forward using radiobiological modeling