Abstract
BACKGROUND: Current automated planning solutions are calibrated using trial and error or machine learning on historical datasets. Neither method allows for the intuitive exploration of differing trade-off options during calibration, which may aid in ensuring automated solutions align with clinical preference. Pareto navigation provides this functionality and offers a potential calibration alternative. The purpose of this study was to validate an automated radiotherapy planning solution with a novel multi-dimensional Pareto navigation calibration interface across two external institutions for prostate cancer. METHODS: The implemented 'Pareto Guided Automated Planning' (PGAP) methodology was developed in RayStation using scripting and consisted of a Pareto navigation calibration interface built upon a 'Protocol Based Automatic Iterative Optimisation' planning framework. 30 previous patients were randomly selected by each institution (I(A) and I(B)), 10 for calibration and 20 for validation. Utilising the Pareto navigation interface automated protocols were calibrated to the institutions' clinical preferences. A single automated plan (VMAT(Auto)) was generated for each validation patient with plan quality compared against the previously treated clinical plan (VMAT(Clinical)) both quantitatively, using a range of DVH metrics, and qualitatively through blind review at the external institution. RESULTS: PGAP led to marked improvements across the majority of rectal dose metrics, with D(mean) reduced by 3.7 Gy and 1.8 Gy for I(A) and I(B) respectively (p < 0.001). For bladder, results were mixed with low and intermediate dose metrics reduced for I(B) but increased for I(A). Differences, whilst statistically significant (p < 0.05) were small and not considered clinically relevant. The reduction in rectum dose was not at the expense of PTV coverage (D(98%) was generally improved with VMAT(Auto)), but was somewhat detrimental to PTV conformality. The prioritisation of rectum over conformality was however aligned with preferences expressed during calibration and was a key driver in both institutions demonstrating a clear preference towards VMAT(Auto), with 31/40 considered superior to VMAT(Clinical) upon blind review. CONCLUSIONS: PGAP enabled intuitive adaptation of automated protocols to an institution's planning aims and yielded plans more congruent with the institution's clinical preference than the locally produced manual clinical plans.