###### Abstract
Nevertheless, it is an _a posteriori_ multicriteria optimisation (MCO) method allowing exploration of the trade-off relationships between PGs [22,25,26]. Recent work has demonstrated the utility of PGAP in yielding plans consistent with oncologists' preferences for prostate patients with and without elective nodal irradiation under conventional and extreme hypofractional regimes [16,22,27]. Despite advances in available calibration methods, RBP calibration takes a 'one size fits all' approach with a single AP protocol (or wishlist) used for all patients of a given clinical site. This assumes an AP calibration that achieves a clinically optimum dose distribution for one patient is optimal for all patients within that clinical site. The validity of a 'one size fits all' approach has not been explicitly explored in the literature and there is evidence that points to site-specific RBP leading to sub-optimal or clinically unacceptable plans for a reasonably large proportions of cases. For lung stereotactic body radiotherapy, [PERSON] et al. observed that up to 24% of automated plans were considered clinically unacceptable without further tweaking [28]. For locally advanced nasopharyngeal carcinoma, [PERSON] et al. conclude that \"automatic VMAT is not good enough to completely replace manual VMAT\" [29]. Finally, though independent quality assurance of 229 prostate cancer patients planned using AP, [PERSON] et al. demonstrated that 17% of plans were suboptimal and could be improved [30]. This evidence highlights deficiencies in the 'one size fits all' approach and indicates that personalisation of AP protocols to individual patients may be required to ensure optimality. In contrast, KBP utilises a fully individualised approach, with ML models using anatomy based predictive factors to generate patient-specific optimisation objectives or dose distribution parameters. The predicted parameters are used to form static objective function inputs to a standard gradient decent optimisation. Whilst optimisations using this approach are inherently patient tailored, the relationship between anatomy and objectives/dose parameters is complex, with wide variances across a patient cohort. Accurate modelling is therefore challenging, generally requires large training datasets and can yield models with clinically relevant prediction errors [31]. Furthermore, the quality of the model is highly depended on the optimality of the underlying training dataset [32], which is not guaranteed. In summary, modelling uncertainties for KBP and the 'one size fits all' approach for RBP mean current AP solutions may not yield optimal, patient tailored plans. To address this problem we propose a hybrid AP solution where KBP is utilised to predict patient specific AP protocol parameters that act as an input for an already validated RBP solution. In this regard RBP is no longer reliant on a 'one size fits all' set of protocol parameters, but instead can utilise a protocol fully personalised to the individual patient. Application of KBP in this manner has the advantage that a validated RBP approach, by its nature, has suitably suppressed the relationship between anatomy and AP protocol parameters such that a single parameter set can yield acceptable plans across a treatment site. In this regard, the purpose of KBP is not to ensure RBP yields acceptable plans, but rather to further refine and individualise AP protocol parameters with the aim of fully personalising treatment plans. Importantly, with much of the variance already reduced through RBP, it is theorised that unlike standalone KBP approaches, uncertainties in the KBP models in a hybrid solution will be of low clinical significance. The purpose of this work was to develop and evaluate a novel KBP-RBP hybrid planning solution for prostate cancer using PGAP. This new methodology utilised ML to identify the relationships between anatomy and optimum patient-specific calibration parameters (determined via Pareto navigation) such that individualised AP protocols could be generated for novel patients.