Abstract
Even with advanced inverse-planning techniques, radiation treatment plan opti-mization remains a very time-consuming task with great output variability, which prompted the development of more automated approaches. One commercially available technique mimics the actions of experienced human operators to pro-gressively guide the traditional optimization process with automatically created regions of interest and associated dose-volume objectives. We report on the initial evaluation of this algorithm on 10 challenging cases of locoreginally advanced head and neck cancer. All patients were treated with VMAT to 70 Gy to the gross disease and 56 Gy to the elective bilateral nodes. The results of post-treatment autoplanning (AP) were compared to the original human-driven plans (HDP). We used an objective scoring system based on defining a collection of specific dosimetric metrics and corresponding numeric score functions for each. Five AP techniques with different input dose goals were applied to all patients. The best of them averaged the composite score 8% lower than the HDP, across the patient population. The difference in median values was statistically significant at the 95% confidence level (Wilcoxon paired signed-rank test p = 0.027). This result reflects the premium the institution places on dose homogeneity, which was consistently higher with the HDPs. The OAR sparing was consistently better with the APs, the differences reaching statistical significance for the mean doses to the parotid glands (p < 0.001) and the inferior pharyngeal constrictor (p = 0.016), as well as for the maximum doses to the spinal cord (p = 0.018) and brainstem (p = 0.040). If one is prepared to accept less stringent dose homogeneity criteria from the RTOG 1016 protocol, nine APs would comply with the protocol, while providing lower OAR doses than the HDPs. Overall, AP is a promising clinical tool, but it could benefit from a better process for shifting the balance between the target dose coverage/homogeneity and OAR sparing.