Background and purpose: With the advent of automatic treatment planning options like Pinnacle’s Autoplanning (PAP), the challenge arises how to assess the quality of a plan that no dosimetrist did work on. The aim of this study was to assess plan quality consistency of PAP prostate cancer patients in clinical practice. Materials and methods: 100 prostate cancer patients were included from NKI and 129 from RadboudUMC (RUMC). Per institute a previously developed [1] treatment planning QA model, based on overlap volume histograms, was trained on PAP plans to predict achievable dose metrics which were then compared to the clinical PAP plans. A threshold of 3 Gy (DVH dose parameters)/3% (DVH volume parameters) was used to detect outliers. For the outlier plans, the PAP technique was adjusted with the aim of meeting the threshold. Results: The average difference between the prediction and the clinically achieved value was <0.5 Gy (mean dose parameters) and <1.2% (volume parameters), with standard deviation of 1.9 Gy/1.5% respectively. We found 8% (NKI)/25% (RUMC) of patients to exceed the 3 Gy/3% threshold, with deviations up to 6.7 Gy (mean dose rectum) and 6% (rectal wall V64Gy). In all cases the plans could be improved to fall within the thresholds, without compromising the other dose metrics. Conclusion: Independent treatment planning QA was used successfully to assess the quality of clinical PAP in a multi-institutional setting. Respectively 8% and 25% suboptimal clinical PAP plans were detected that all could be improved with replanning. Therefore we recommend the use of independent treatment plan QA in combination with PAP for prostate cancer patients.

Additional Metadata
Keywords Knowledge based planning, Autoplanning
Persistent URL dx.doi.org/10.1016/j.radonc.2018.10.035, hdl.handle.net/1765/116097
Journal Radiotherapy & Oncology
Citation
Janssen, T.M., Kusters, M., Wang, YB, Wortel, G., Monshouwer, R., Damen, E., & Petit, S.F. (2019). Independent knowledge-based treatment planning QA to audit Pinnacle autoplanning. Radiotherapy & Oncology, 133, 198–204. doi:10.1016/j.radonc.2018.10.035