## 4 Discussion  
In our previous work we developed a PGAP solution (built on a PBAIO framework) that utilised a single 'one size fits all' AP protocol for all patients in a given treatment site. The approach was evaluated against traditional TAE manual planning and considered non-inferior. This study builds upon that work in two key ways. Firstly, we introduced ML upstream of the PBAIO AP algorithm to develop a novel hybrid KBP-RBP planning approach, where ML is utilised to generate fully bespoke AP protocols for individual patients. Secondly, PGAP\({}_{\text{std}}\), PGAP-ML\({}_{\text{clus}}\) and PGAP-ML\({}_{\text{reg}}\) were evaluated against a Pareto navigated gold standard, rather than traditional TAE manual planning that is prone to sub optimality [53]. In this regard the efficacy of each automated approach could be comprehensively assessed. Plans generated from this novel approach and plans generated via PGAP\({}_{\text{std}}\) were compared to a Pareto navigation gold standard (\(\text{MCO}_{\text{gs}}\)). All approaches yielded plans acceptable for clinical use and at a population level demonstrated excellent congruence with \(\text{MCO}_{\text{gs}}\). At an individual patient level, PGAP-ML\({}_{\text{reg}}\) was considered the weakest solution, due to algorithms being influenced by anatomical outliers. Both PGAP\({}_{\text{std}}\) and PGAP-ML\({}_{\text{clus}}\) yielded very good agreement with \(\text{MCO}_{\text{gs}}\) across all patients, with PGAP-ML\({}_{\text{clus}}\) considered marginally superior due to fewer extreme outliers. ML techniques used in this work are not new to radiotherapy planning. PCA [5], regression [5; 50] and clustering [54] have all been used in KBP to make predictions based on anatomical features with notable success. This work builds upon this knowledge in two ways. Firstly, previous ML implementation would typically seek to generate a patient-specific input to a native treatment planning optimiser. In contrast this novel approach aimed to generate patient-specific AP protocols to further personalise an already validated RBP solution. Secondly we present a methodology to evaluate the performance of different model formations using a LOOCV decision framework, such that the optimal model for a given site can be selected. This allowed for an automatic and unbiased choice among different models comprised of various feature sets, types of features and types of model. This approach helps to resolve the challenge of defining a ML formation prior to training  
Figure 5: Transverse CT slice of the first validation patient (patient 21) showing dose distributions for each calibration method. Delineated volumes are rectum (brown), PTV60 (red) and PTV48 (orange). Tiles are: (**a**) \(\text{MCO}_{\text{gs}}\), (**b**) PGAP\({}_{\text{std}}\), (**c**) PGAP-ML\({}_{\text{reg}}\) and (**d**) PGAP-ML\({}_{\text{clus}}\). and allows for bespoke architecture to be utilised for individual PCs, thus removing the requirement for a homogeneous ML approach, which may not be appropriate. Results of this study support this assertion, with different model formations selected during the LOOCV model selection process. ML in this work relied on a dataset of numerical geometric information derived from delineated patient anatomy. Whilst this methodology is based on previous KBP work, inclusion of other features may improve the versatility and modelling accuracy of the developed approach. A promising method would be utilisation of neural network generated features, which has been implemented successfully for dose prediction [55; 56]. Neural networks could be utilised to directly generate patient-specific AP protocols or used in a two step approach to generate dosimetric features (rather than anatomical features) from which PG weights are derived [57]. However, as plan generation is a geometry-based optimisation problem, modelling wholly on anatomy based features may hold intrinsic value as they can be interpreted and therefore reduce the risk of developing an automated planning 'black box'.