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Review
. 2023 May 11;68(10):10.1088/1361-6560/accdb2.
doi: 10.1088/1361-6560/accdb2.

Online adaptive planning methods for intensity-modulated radiotherapy

Zihang Qiu  1   2 , Sven Olberg  2 , Dick den Hertog  1 , Ali Ajdari  2 , Thomas Bortfeld  2 , Jennifer Pursley  2
Affiliations
Review

Online adaptive planning methods for intensity-modulated radiotherapy

Zihang Qiu et al. Phys Med Biol. .

Abstract

Online adaptive radiation therapy aims at adapting a patient's treatment plan to their current anatomy to account for inter-fraction variations before daily treatment delivery. As this process needs to be accomplished while the patient is immobilized on the treatment couch, it requires time-efficient adaptive planning methods to generate a quality daily treatment plan rapidly. The conventional planning methods do not meet the time requirement of online adaptive radiation therapy because they often involve excessive human intervention, significantly prolonging the planning phase. This article reviews the planning strategies employed by current commercial online adaptive radiation therapy systems, research on online adaptive planning, and artificial intelligence's potential application to online adaptive planning.

Keywords: IMPT; IMRT; online adaptive radiation therapy; online adaptive treatment planning.

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Figures

Figure 1.
Figure 1.
Review structure
Figure 2.
Figure 2.
Plan deformation methods derive a deformation field from the inter-fraction anatomical changes that have occurred between day 0 and day n. The deformation field is then applied to the original plan for daily plan generation, in hopes that the original plan-anatomy relationship can be retained.
Figure 3.
Figure 3.
2D demonstration of the Zarepisheh et al. (2014) method in the objective space. The yellow and red dots represent the pivot and reference plans. The dashed and solid curves represent the quadratically approximated and actual daily Pareto frontier, the arrow represents the objective weights of the reference plan on the quadratically approximate Pareto frontier, which leads to the daily plan, the green dot, on the actual daily Pareto frontier.
Figure 4.
Figure 4.
Due to cavity filling, a proton beamlet planned for the day 0 anatomy becomes short in range. After proton energy modulation, the proton beamlet is placed back at the same position.
Figure 5.
Figure 5.
Three classes of the AI methods that have the potential for online adaptive planning.
See this image and copyright information in PMC

References

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