Introduction:  Despite the vast amount of optimization algorithms, radiotherapy treatment planning remains a manual, time-consuming and iterative process. To increase plan standardization, we clinically use Pinnacle's autoplanner for several disease sites. However, this introduces new challenges: first, the autoplanner is not perfect and still requires substantial interaction from the radiotherapy technician (RTT). Second, it is difficult to judge whether a plan has indeed the most optimal trade-o_ between cure and toxicity, since the RTT has not worked the plan. Knowledge-based planning (KBP) could serve as a quality assurance tool to resolve these problems. It uses historical data (anatomical and dosimetric) from previous plans, to predict the likely dose distribution for the current patient. In this study, we construct an initial, simplistic KBP model that serves as the clinical practice. We then investigate of a variety of KBP modelling approaches to predict rectum dose-volume histograms (DVHs), in order to complement the current clinical practice in prostate cancer. Methods:  For model evaluation, we formulate a clinical tolerance criterion (TC) bandwidth based on a ground-truth set of existing radiotherapy plans. We evaluate on the overall prediction accuracy (RMS), the fraction of correctly predicted DVH bins (TCα), anαd on the fraction of patients that have ≥ 90% of their DVH correctly predicted (TCβ). We use the overlap volume histogram (OVH) to encode for organ geometrical information, and use reduced order modelling (ROM) to capture the most important characteristics of the DVH and OVH. Optimization methods we use are Principal Component Analysis (PCA) eigenvalue RMS minimization, direct DVH RMS minimization, and TCα and TCβ maximization. Results:  Analyses of the KBP clinical practice yielded training and testing errors of 81.4% and 80.8% for TCα and 53.3% and 51.1% for TCβ, with an RMS of 4.80 and 4.94 volume percentage [%]. Eigenvalue-optimization resulted TCα of 86.5% and 82.4%, and TCβ of 68.8% and 59.1%, with respective RMS of 2.82 % and 3.22 %. Direct DVH-optimization yielded TCα of 86.7% and 81.9%, and TCβ of 69.4% and 61.4%, with similar RMS. TCα and TCβ maximizers resulted TCα training and testing errors of 92.1% and 78.5%, and TCβ training and testing errors of and 84.3% and 53.4% respectively. Discussion:  The investigated models yielded significant improvements for direct eigenvalue- and DVH optimization methods. We have also been able to perform optimizations for the clinical goal metrics, showing promising results in training data. Because TCα- and TCβ- maximizers were unable generalize to perform well for unseen data, it is believed these metrics are too sensitive to be trained reliably, and more consistent data may be required for these optimizers to produce reliable test errors. Based on our findings, we advice the clinical practice to extend KBP-approaches to optimize for DVH-least squares.

Radiotherapy treatment planning is a complex and time consuming process prone to differences as result of choices of individual planners. Autoplanning systems have been introduced to both reduce the time consumption and to counteract the influence of individual planning choices. Although autoplanning generally increases performance of the treatment plans, the plans still need to be checked manually to ensure plan accuracy. Increasing plan consistency with knowledge based planning (KBP) or automatic plan evaluation would be clear ways to improve upon this problem. For both improvements, deep learning can be an important tool to produce accurate and fast 3D dose distributions. In this study such a deep learning tool is developed in two parts to accommodate this. In the first part a structure based deep learning model, using a U-Net architecture is developed, used for dose distribution prediction for prostate patients treated with volumetric modulated arc therapy (VMAT) plans, generated by autoplanning. Different loss functions have been tested to see which achieve the best results. In the second part physics information is included in the neural network prediction by using a hybrid physics-data approach. For this, an approximate dose distribution, the segment dose, is used as extra input for the dose prediction network. This segment dose is created by predicting multi leaf collimator (MLC) positions and reconstructing the corresponding dose with a simple dose engine. The predictions are evaluated using several dose characteristics, dose volume histogram (DVH) curves and other clinically relevant metrics. The U-Net architecture proved able to accurately predict the dose of patients in the test set. The best performing loss function for accurate dose characteristics prediction was found to be the weighted mean squared error (WMSE) loss, which predicted several PTV DVH statistics within a 1.5 % error and the rectum statistics within 3.5 % error. Furthermore, the model predicts the DVH points of the PTV within 0.84 ± 0.40 Gy average absolute dose difference and the rectum in 0.91 ± 0.72 Gy average dose difference. The hybrid physics-data model did not improve the prediction accuracy of the dose prediction model in terms of DVH statistics and DVH prediction, as the MLC positions could not be predicted accurately enough. The performance of the structure based prediction is similar to the performance of state-of-the-art prediction models. Although the hybrid model with the predicted segment doses did not improve the prediction accuracy, a significant effect could be seen from using the correct MLC positions instead. The bottleneck of the prediction is therefore identified to be the segment prediction. As such, it would be interesting to focus on segment prediction in follow up research.