## 5 Conclusions  
A machine-learning methodology for generating patient-specific AP protocols via clustering and regression was developed and validated for prostate cancer. Unlike current RBP approaches, which use a 'one size fits all' site-specific protocol, this novel KBP-RBP hybrid approach sought to fully personalise the automated planning process. The relationships between anatomy and AP calibration parameters was explored, with key predictive features identified for each optimisation planning goal. Compared to site-specific protocols, patient-specific protocols offered minimal advantage, with both approaches yielding plans of nominal equivalence to gold standard plans generated via a posteriori multicriteria optimisations. Future work should include application to additional treatment sites, training on datasets activity (rather than randomly) curated to represent the broad patient population and if practicable, replication of findings using larger datasets.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13074548/s1](https://www.mdpi.com/article/10.3390/app13074548/s1)]([https://www.mdpi.com/article/10.3390/app13074548/s1](https://www.mdpi.com/article/10.3390/app13074548/s1)), File S1: Distribution of relative weighting factors; Table S1: Full list of features contained in FeatureDS1; Table S2: List of features contained in FeatureDS2; Table S3: Relative weights of \(\text{MCO}_{\text{gs}}\); Table S4: Relative weights of \(\text{PGAP}_{\text{std}}\); Table S5: Relative weights of \(\text{PGAP}\text{-ML}_{\text{reg}}\); Table S6: Relative weights of \(\text{PGAP}\text{-ML}_{\text{clus}}\); Table S7: Dosimetric features of \(\text{MCO}_{\text{gs}}\); Table S8: Dosimetric features of \(\text{PGAP}_{\text{std}}\); Table S9: Dosimetric features of \(\text{PGAP}\text{-ML}_{\text{reg}}\); Table S10: Dosimetric features of \(\text{PGAP}\text{-ML}_{\text{clus}}\).  
Conceptualization P.W.; methodology, I.F. and P.W.; software, I.F.; validation, I.F. and P.W.; formal analysis, I.F.; investigation, I.F.; resources, I.F. and P.W.; data curation, I.F.; writing--original draft preparation, I.F.; writing--review and editing, I.F., P.W. and E.S.; visualization, I.F.; supervision, P.W. and E.S.; project administration, E.S.; funding acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Velindre's Advancing Radiotherapy Fund.  
Not applicable.  
Patient consent was waived as this was a simulation study performed on fully anonymised patient data.  
Data provided within Supplementary Files.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
Degree of a polynomial model, e.g., a quadratic model has two degrees  
Fature Anatomical variable that defines a geometric characteristic relating to regions-  
of-interest. May be used in raw form or as Principal Components  
FeatureDS1  
Dataset of all raw Features used for generating Principal Components. Contains no variables with missing data or low variance  
FeatureDS2  
A subset of FeatureDS1. No pair of Features in this dataset has a correlation coefficient greater than 0.85  
Type of model, e.g., 2 features 3 degrees regression or 15 clusters. Formation of the model irrespective of the feature types used.  
OV\({}_{\text{'ROI'},\text{'ROI'}}\)  
Sub-region defined by the overlap of two regions-of-interest (i.e., ROI1 and ROI2). Measured in cm3  
PTV\({}_{\text{'X'}}\)cm  
PTV expanded isotropically by 'x' cm, e.g., PTV48\({}_{0.02}\)cm is PTV48 + 0.02 cm