###### Abstract  
Automated planning (AP) uses common protocols for all patients within a cancer site. This work investigated using machine learning to personalise AP protocols for fully individualised planning. A 'Pareto guided automated planning' (PGAP) solution was used to generate patient-specific AP protocols and gold standard Pareto navigated reference plans (\(\text{MCO}_{\text{gs}}\)) for 40 prostate cancer patients. Anatomical features related to geometry were extracted and two ML approaches (clustering and regression) that predicted patient-specific planning goal weights were trained on patients 1-20. For validation, three plans were generated for patients 21-40 using a standard site-specific AP protocol based on averaged weights (\(\text{PGAP}_{\text{std}}\)) and patient-specific AP protocols generated via regression (\(\text{PGAP-ML}_{\text{reg}}\)) and clustering (\(\text{PGAP-ML}_{\text{clus}}\)). The three methods were compared to \(\text{MCO}_{\text{gs}}\) in terms of weighting factors and plan dose metrics. Results demonstrated that at the population level \(\text{PGAP}_{\text{std}}\), \(\text{PGAP-ML}_{\text{reg}}\) and \(\text{PGAP-ML}_{\text{clus}}\) provided excellent correspondence with \(\text{MCO}_{\text{gs}}\). Deviations were either not statistically significant (\(p\geq 0.05\)), or of a small magnitude, with all coverage and hotspot dose metrics within 0.2 Gy of \(\text{MCO}_{\text{gs}}\) and OAR metrics within 0.7% and 0.4 Gy for volume and dose metrics, respectively. When compared to \(\text{PGAP}_{\text{std}}\), patient-specific protocols offered minimal advantage for this cancer site, with both approaches highly congruent with \(\text{MCO}_{\text{gs}}\). automated planning; multicriteria optimisation; Pareto optimisation; prostate cancer +
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non-inferior to manual planning and are used in clinical practice. Comprehensive reviews of all techniques are found in the literature [1,2,4]. The most clinically desirable plans are 'Pareto optimal'. That is, no dosimetric improvements can be made to a PG except at the detriment of another. The various AP methods therefore aim to converge upon this set. However, planning can be complex given PGs may conflict with one another and clinical desirability is dependent upon appropriate management of these trade-offs. Therefore, although the most clinically desirable plans are Pareto optimal, achieving Pareto optimality does not guarantee clinical desirability. For KBP, trade-off balancing is automatically determined by the underlying clinical plans in the knowledge-base. For RBP, balancing must be explicitly defined in a process known as 'calibration'. Calibration is the process of balancing the relative priority of PGs such that they align with the oncologists' preferences. The dominant approach to RBP calibration is trial-and-error [19, 20, 21] (TAE) where AP parameters are iteratively updated until an acceptable solution for a given clinical site is obtained. The approach is time consuming with improvements made only with respect to previously tried examples. It does not allow for the intuitive exploration of competing PGs and, as with manual planning, may yield solutions that are not fully congruent with oncologists' clinical preferences [22]. One way to manage the limitations of TAE is to use a KBP calibration approach where AP calibrations are derived from machine learning (ML) on historical clinical datasets [23,24]. This approach may be more efficient than TAE but will depend strongly on the knowledge base composition. A third approach is to utilise Pareto navigation techniques during the calibration process ('Pareto guided automated planning' or PGAP). This involves exploring a set of unique and systematically produced Pareto optimal solutions, each representing a differently balanced AP solution. Due to the number of solutions necessary for this to be effective, it can be resource intensive.