Automated 4π radiotherapy treatment planning with evolving knowledge-base

Abstract

Purpose: Non-coplanar 4π radiotherapy generalizes intensity modulated radiation therapy (IMRT) to automate beam geometry selection but requires complicated hyperparameter tuning to attain superior plan quality, which can be tedious and inconsistent. In this study, a fully automated 4π treatment planning was developed using evolving knowledge-base (EKB) planning guided by dose prediction.

Methods: Twenty 4π lung and twenty 4π head and neck (HN) cases were included. A statistical voxel dose learning model was initially trained on low-quality plans created using generic hyperparameter templates without manual tuning. To improve the automated plan quality without being limited by the training data quality, a new 4π optimization problem was formulated to include a one-sided penalty on the organ-at-risk (OAR) dose deviation from the predicted dose. This directional OAR penalty encourages superior OAR sparing. The fast iterative shrinkage-thresholding algorithm (FISTA) was used to solve the large-scale beam orientation optimization problem. With the improved plans, new predictions were created to guide the next loop of EKB planning for a total of 10 loops. Plan quality was evaluated using a plan quality metric (PQM) points system based on clinical dose constraints and compared with automated planning approaches guided by manual high-quality plans using all non-coplanar beams, automated plans using individually evolved targeted dose, and manually created 4π plans.

Results: For the lung cases, the final EKB plans had significantly higher PQM than manually created 4π (+2.60%). The improvements plateaued after the third loop. The final HN EKB plans and manually created 4π plans had comparable PQMs, but had lower PQM compared to automated plans using a high-quality training set (-3.00% and -4.44%, respectively). The PQM consistently increased up to the sixth loop. Individually evolved plans were able to improve the plan quality from initial condition due to the one-sided cost function but the 60% of them were trapped in undesired local minima that were substantially worse than their corresponding EKB plans.

Conclusion: Evolving knowledge-base planning is a novel automated planning technique guided by the predicted three-dimensional dose distribution, which can evolve from low-quality plans. EKB allows new beams to be used in the automated planning workflow for superior plan quality.

Keywords: 4π; automated treatment planning; evolution.

Figure 1

Flowchart of the evolving knowledge‐base framework for automated planning. The initial plans use a generic hyperparameter template to jump‐start the EKB planning. In this study, this process was performed for 10 loops (shown in blue). FISTA iterations within each automated planning optimization are shown in orange. FISTA, fast iterative shrinkage‐thresholding algorithm. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 2

(a,b) Lung and (c,d) head and neck plan quality metric (PQM) results. (a,c) PQM for each evolving knowledge‐base (EKB) planning loop for 20 patients. (b,d) Boxplots of the PQM of 20 patients for the final EKB plans, automated planning using high‐quality plans in the training set (autHQ), the final individually evolving (IE) plans, and manually created 4π plans. Significant (P < 0.05) differences between pairs of plans are labeled by the horizontal black lines. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 3

Dose–volume histogram comparison between the initial EKB plan, made with a generic template, and the final EKB plan for an example HN case. Only representative OARs are shown to reduce clutter. EKB, evolving knowledge‐base. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 4

Beam orientation evolution for representative EKB loops, autHQ, and manual 4π for an example HN case. EKB, evolving knowledge‐base; HN, head and neck. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 5

Dose–volume histogram comparison between final EKB, autHQ, final IE, and manual plans of an example lung case. EKB, evolving knowledge‐base; IE, individually evolving. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 6

Dose–volume histogram comparison between final EKB, autHQ, final IE, and manual plans of an example head and neck case. Only representative OARs are shown to reduce clutter. EKB, evolving knowledge‐base; IE, individually evolving. [Color figure can be viewed at wileyonlinelibrary.com]

Figure 7

Dosimetric results for 20 lung plans each of (in order from left to right) the final EKB, autHQ, final IE, and manual plans. Significant (P < 0.05) differences between pairs of plans are labeled by the horizontal black lines. EKB, evolving knowledge‐base; IE, individually evolving.

Figure 8

Dosimetric results for 20 head and neck plans each of (in order from left to right) the final EKB, autHQ, final IE, and manual 4π plans. Significant (P < 0.05) differences between pairs of plans are labeled by the horizontal black lines. EKB, evolving knowledge‐base; IE, individually evolving. [Color figure can be viewed at wileyonlinelibrary.com]