Advances in Automated Treatment Planning

Abstract

Treatment planning in radiation therapy has progressed enormously over the past several decades. Such advancements came in the form of innovative hardware and algorithms, giving rise to modalities such as intensity-modulated radiation therapy and volume modulated arc therapy, greatly improving patient outcome and quality of life. While these developments have improved the overall plan quality, they have also given rise to higher treatment planning complexity. This has resulted in increased treatment planning time and higher variability in the final approved plan quality. Radiation oncology, as an already technologically advanced field, has much research and implementation involving the use of AI. The field has begun to show the efficacy of using such technologies in many of its sub-areas, such as in diagnosis, imaging, segmentation, treatment planning, quality assurance, treatment delivery, and follow-up. Some AI technologies have already been clinically implemented by commercial systems. In this article, we will provide an overview to methods involved with treatment planning in radiation therapy. In particular, we will review the recent research and literature related to automation of the treatment planning process, leading to potentially higher efficiency and higher quality plans. We will then present the current and future challenges, as well as some future perspectives.

Figure 1.

Dose washes of an example test patient from the study by Nguyen et al.. The colorbar is shown in units of Gy. The clinical ground truth dose is shown on the top row, followed by the dose predictions of the proposed model, HD U-net, and two other comparative models, Standard U-net and DenseNet. Low dose cutoff for viewing was chosen to be 5% of the highest prescription dose (3.5 Gy).

Figure 2:

Workflow of the proposed fluence map prediction method. The leftmost figure represents the projections of dose in phantom geometry.

Figure 3:

Evolution of: (a) dose volume histograms and dose distributions, (b) Treatment planning parameter (TPP) weight values(λ and τ), and (c) and original and modified ProKnow plan scores in the planning process of a test patient case using our DRL-based virtual treatment planner network (VTPN). ProKnow plan score is a scoring system for prostate cancer IMRT plan, using scores for different clinical criteria (ProKnow Systems,Sanford, FL, USA).