On adaptation cost and tractability in robust adaptive radiation therapy optimization

Abstract

Adaptation Cost and Tractability in Robust ARTPurpose: In this paper, a framework for online robust adaptive radiation therapy (ART) is discussed and evaluated. The purpose of the presented approach to ART is to: (i) handle interfractional geometric variations following a probability distribution different from the a priori hypothesis, (ii) address adaptation cost and (iii) address computational tractability.Methods: A novel framework for online robust ART using the concept of Bayesian inference and scenario-reduction is introduced and evaluated in a series of treatment on a one-dimensional phantom geometry. The initial robust plan is generated from a robust optimization problem based on either expected-value-or worst-caseoptimization approach using the a priori hypothesis of the probability distribution governing the interfractional geometric variations. Throughout the course of every treatment, the simulated interfractional variations are evaluated in terms of their likelihood with respect to the a priori hypothesis of their distribution and violation of user-specified tolerance limits by the accumulated dose. If an adaptation is considered, the a posteriori distribution is computed from the actual variations using Bayesian inference. Then, the adapted plan is optimized to better suit the actual interfractional variations of the individual case. This adapted plan is used until the next adaptation is triggered. To address adaptation cost, the proposed framework provides an option for increased adaptation frequency. Computational tractability in robust planning and ART is addressed by approximation algorithms to reduce the size of the optimization problem.Results: According to the simulations, the proposed framework may improve target coverage compared to the corresponding non-adaptive robust approach. In particular, combining the worst-case-optimization approach with Bayesian inference may perform best in terms of improving CTV coverage and organ-at-risk (OAR) protection. Concerning adaptation cost, the results indicate that mathematical methods like Bayesian inference may have a greater impact on improving individual treatment quality than increased adaptation frequency. In addition, the simulations suggest that the concept of scenario-reduction may be useful to address computational tractability in ART and robust planning in general. Conclusion:The simulations indicate that the adapted plans may improve target coverage and OAR protection at manageable adaptation and computational cost

Assessing the need for adaptive radiotherapy in head and neck cancer patients using an automatic planning tool

“…However, one of the main limitations of IGRT is that internal changes in size, shape or relative position of the target volumes or OARs compared with the initial p-CT cannot be corrected by rigid translational and/or rotational couch shifts [7]. A possible solution to this problem is Adaptive Radiotherapy (ART), which aims at correcting anatomical modifications by adapting the initial dose plan to the current patient status [8,9]. This process requires repeated imaging with sufficient quality for treatment planning, re-contouring and re-planning, and can be done either offline, between treatment fractions, or online immediately prior to a fraction while the patient is lying on the treatment couch [9,10].…”

“…A possible solution to this problem is Adaptive Radiotherapy (ART), which aims at correcting anatomical modifications by adapting the initial dose plan to the current patient status [8,9]. This process requires repeated imaging with sufficient quality for treatment planning, re-contouring and re-planning, and can be done either offline, between treatment fractions, or online immediately prior to a fraction while the patient is lying on the treatment couch [9,10]. The most frequently implemented type of ART is offline ART, where the needed ad-justments are done between treatment sessions, since online ART (during a given treatment session) requires advanced tools, such as automatic segmentation and planning, which are not commonly available for most centres.…”

Triggering daily online adaptive radiotherapy in the pelvis: Dosimetric effects and procedural implications of trigger parameter‐value selection

“…49 An early combination of ART and plan robustness is described by Birkner et al, 50 while Böck presents a framework for online robust ART including consideration of adaptation cost and frequency. 51 Similarly, Liu et al combine robust plan optimization and ART by using images acquired during the course of treatment to update the statistics that drive the robust optimization. 52 This interaction between ART, margins, and robust optimization should continue to be explored as their optimal use is intertwined and dependent on many specific treatment planning and delivery considerations.…”

“…Anatomic variations can also be considered more explicitly during robust plan optimization 49 . An early combination of ART and plan robustness is described by Birkner et al., 50 while Böck presents a framework for online robust ART including consideration of adaptation cost and frequency 51 . Similarly, Liu et al.…”

“… 49 An early combination of ART and plan robustness is described by Birkner et al., 50 while Böck presents a framework for online robust ART including consideration of adaptation cost and frequency. 51 Similarly, Liu et al. combine robust plan optimization and ART by using images acquired during the course of treatment to update the statistics that drive the robust optimization.…”