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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works
A GPU-based multi-criteria optimization algorithm for HDR brachytherapy
Currently in HDR brachytherapy planning, a manual fine-tuning of an objective
function is necessary to obtain case-specific valid plans. This study intends
to facilitate this process by proposing a patient-specific inverse planning
algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization
(gMCO).
Two GPU-based optimization engines including simulated annealing (gSA) and a
quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in
parallel. After evaluating the equivalence and the computation performance of
these two optimization engines, one preferred optimization engine was selected
for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR
cases were divided into validation set (100) and test set (462). In the
validation set, the number of Pareto optimal plans to achieve the best plan
quality was determined for the gMCO algorithm. In the test set, gMCO plans were
compared with the physician-approved clinical plans.
Over 462 cases, the number of clinically valid plans was 428 (92.6%) for
clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with
target V100 coverage greater than 95% was 288 (62.3%) for clinical plans and
414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO
algorithm to generate 1000 Pareto optimal plans.
In conclusion, gL-BFGS is able to compute thousands of SA equivalent
treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast
and robust multi-criteria optimization algorithm was implemented for HDR
prostate brachytherapy. A large-scale comparison against physician approved
clinical plans showed that treatment plan quality could be improved and
planning time could be significantly reduced with the proposed gMCO algorithm.Comment: 18 pages, 7 figure
Explicit optimization of plan quality measures in intensity-modulated radiation therapy treatment planning
Conventional planning objectives in optimization of intensity-modulated
radiotherapy treatment (IMRT) plans are designed to minimize the violation of
dose-volume histogram (DVH) thresholds using penalty functions. Although
successful in guiding the DVH curve towards these thresholds, conventional
planning objectives offer limited control of the individual points on the DVH
curve (doses-at-volume) used to evaluate plan quality. In this study, we
abandon the usual penalty-function framework and propose planning objectives
that more explicitly relate to DVH statistics. The proposed planning objectives
are based on mean-tail-dose, resulting in convex optimization. We also
demonstrate how to adapt a standard optimization method to the proposed
formulation in order to obtain a substantial reduction in computational cost.
We investigate the potential of the proposed planning objectives as tools for
optimizing DVH statistics through juxtaposition with the conventional planning
objectives on two patient cases. Sets of treatment plans with differently
balanced planning objectives are generated using either the proposed or the
conventional approach. Dominance in the sense of better distributed
doses-at-volume is observed in plans optimized within the proposed framework,
indicating that the DVH statistics are better optimized and more efficiently
balanced using the proposed planning objectives
Optimal partial-arcs in VMAT treatment planning
Purpose: To improve the delivery efficiency of VMAT by extending the recently
published VMAT treatment planning algorithm vmerge to automatically generate
optimal partial-arc plans.
Methods and materials: A high-quality initial plan is created by solving a
convex multicriteria optimization problem using 180 equi-spaced beams. This
initial plan is used to form a set of dose constraints, and a set of
partial-arc plans is created by searching the space of all possible partial-arc
plans that satisfy these constraints. For each partial-arc, an iterative
fluence map merging and sequencing algorithm (vmerge) is used to improve the
delivery efficiency. Merging continues as long as the dose quality is
maintained above a user-defined threshold. The final plan is selected as the
partial arc with the lowest treatment time. The complete algorithm is called
pmerge.
Results: Partial-arc plans are created using pmerge for a lung, liver and
prostate case, with final treatment times of 127, 245 and 147 seconds.
Treatment times using full arcs with vmerge are 211, 357 and 178 seconds. Dose
quality is maintained across the initial, vmerge, and pmerge plans to within 5%
of the mean doses to the critical organs-at-risk and with target coverage above
98%. Additionally, we find that the angular distribution of fluence in the
initial plans is predictive of the start and end angles of the optimal
partial-arc.
Conclusions: The pmerge algorithm is an extension to vmerge that
automatically finds the partial-arc plan that minimizes the treatment time.
VMAT delivery efficiency can be improved by employing partial-arcs without
compromising dose quality. Partial arcs are most applicable to cases with
non-centralized targets, where the time savings is greatest
Sensitivity analysis for lexicographic ordering in radiation therapy treatment planning
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134937/1/mp0218.pd
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