Toward robust adaptive radiation therapy strategies

Abstract

Purpose To set up a framework combining robust treatment planning with adaptive re‐optimization in order to maintain high treatment quality, to respond to interfractional geometric variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods The authors propose robust adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one‐dimensional patient phantom. The plan applied during the first fractions should be able to handle anticipated systematic and random errors. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient having received a dose that does not satisfy specified plan quality criteria, the plan is re‐optimized based on these individually measured errors. The re‐optimized plan is then applied during subsequent fractions until a new scheduled adaptation becomes necessary. In this study, three different adaptive strategies are introduced and investigated. (a) In the first adaptive strategy, the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust re‐optimization. (b) In the second strategy, the degree of conservativeness is adapted in response to the measured dose delivery errors. (c) In the third strategy, the uncertainty margins around the target are recalculated based on the measured errors. The simulated treatments are subjected to systematic and random errors that are either similar to the anticipated errors or unpredictably larger in order to critically evaluate the performance of these three adaptive strategies. Results According to the simulations, robustly optimized treatment plans provide sufficient treatment quality for those treatment error scenarios similar to the anticipated error scenarios. Moreover, combining robust planning with adaptation leads to improved organ‐at‐risk protection. In case of unpredictably larger treatment errors, the first strategy in combination with at most weekly adaptation performs best at notably improving treatment quality in terms of target coverage and organ‐at‐risk protection in comparison with a non‐adaptive approach and the other adaptive strategies. Conclusion The authors present a framework that provides robust plan re‐optimization or margin adaptation of a treatment plan in response to interfractional geometric errors throughout the fractionated treatment. According to the simulations, these robust adaptive treatment strategies are able to identify candidates for an adaptive treatment, thus giving the opportunity to provide individualized plans, and improve their treatment quality through adaptation. The simulated robust adaptive framework is a guide for further development of optimally controlled robust adaptive therapy models.

Quantification of accumulated dose and associated anatomical changes of esophagus using weekly Magnetic Resonance Imaging acquired during radiotherapy of locally advanced lung cancer

“…Ultimately, the distribution of these errors can be retrospectively modelled as a Probability Density Function and incorporated into an optimization framework to further reduce the dose to the esophagus. Such a development would pave a road for an adaptive robust optimization approach for treatment planning to reduce AE in lung cancer patients [30].…”

On adaptation cost and tractability in robust adaptive radiation therapy optimization

“…The relative volumes in every ROI satisfy n∈Nr v n,r = 1. To emphasize the mathematical properties of this framework, a convex objective function as given by (7) is chosen to exploit its smoothness, convexity and the resulting globally optimal solutions of (5) and (6). Thus, the resulting initial and adapted robust plans can be interpreted in a straightforward manner.…”

“…Theoretical studies focus on the problem formulation and optimization approach. According to our previous studies 5,6 , the concept of combining robust optimization approaches with adaptive replanning can handle interfractional geometric variations. In particular, this combination can outperform the conventional non-adaptive treatment approach in terms of CTV coverage and OAR protection.…”

“…These models were evaluated in mathematical and simulation studies in the context of offline robust ART and compared to the performance of nonadaptive strategies using robust plans or traditional PTV plans. The performance of the traditional PTV plans, using van Herk’s 1D‐margin formula, 2 has been compared to that of the robust approach has been investigated in our previous publication 6 . The results in Böck et al indicate that the robust approach is more likely to provide both target coverage and OAR protection.…”

Hierarchical model-based object localization for auto-contouring in head and neck radiation therapy planning

“…Radiation therapy (RT) planning is performed to reduce radiation-induced toxicities and the risk of secondary malignancies while maximizing therapeutic effects by precisely controlling how ionizing radiation will be administered to designated portions of the body [1]. Segmentation of target structures (e.g., tumor) and organs at risk (OARs) is a key step that is required during the treatment planning process.…”