A multi-criteria optimization approach for HDR prostate brachytherapy: I. Pareto surface approximation

Cui, Songye; Després, Philippe; Beaulieu, Luc

doi:10.1088/1361-6560/aae24c

Cited by 16 publications

(35 citation statements)

References 30 publications

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“…In contrast to a more recently introduced multicriteria optimization approach that requires pretrained regression models to guide the optimization toward clinically acceptable plans, our bi‐objective evolutionary optimization approach requires no prior training, because it directly calculates all relevant DV indices throughout optimization. As our bi‐objective evolutionary optimization approach requires between 30 and 300 seconds, we have shown that it is indeed possible to approximate a surface of trade‐off plans within a clinically acceptable time frame.…”

Section: Discussionmentioning

confidence: 99%

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

et al. 2019

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Purpose The purpose of this study is to improve upon a recently introduced bi‐objective treatment planning method for prostate high‐dose‐rate (HDR) brachytherapy (BT), both in terms of resulting plan quality and runtime requirements, to the extent that its execution time is clinically acceptable. Methods Bi‐objective treatment planning is done using a state‐of‐the‐art multiobjective evolutionary algorithm, which produces a large number of potential treatment plans with different trade‐offs between coverage of the target volumes and sparing organs at risk. A graphics processing unit (GPU) is used for large‐scale parallelization of dose calculations and the calculation of the dose‐volume (DV) indices of potential treatment plans. Moreover, the objectives of the previously used bi‐objective optimization model are modified to produce better results. Results We applied the GPU‐accelerated bi‐objective treatment planning method to a set of 18 patients, resulting in a set containing a few hundred potential treatment plans with different trade‐offs for each of these patients. Due to accelerations introduced in this article, results previously achieved after 1 hour are now achieved within 30 seconds of optimization. We found plans satisfying the clinical protocol for 15 of 18 patients, whereas this was the case for only 4 of 18 clinical plans. Higher quality treatment plans are obtained when the accuracy of DV index calculation is increased using more dose calculation points, requiring still no more than 3 minutes of optimization for 100 000 points. Conclusions Large sets of high‐quality treatment plans that trade‐off coverage and sparing are now achievable within 30 seconds, due to the GPU‐acceleration of a previously introduced bi‐objective treatment planning method for prostate HDR brachytherapy. Higher quality plans can be achieved when optimizing for 3 minutes, which we still consider to be clinically acceptable. This allows for more insightful treatment plan selection in a clinical setting.

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Section: Discussionmentioning

confidence: 99%

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

et al. 2019

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“…2.1.1 Patient selection An anonymous dataset that contains 562 prostate cancer patients who received an HDR brachytherapy treatment as a boost to EBRT from April 2011 to July 2016 at our institution was studied. This dataset incorporates the cases studied in prior works (Edimo et al 2019, Cui et al 2018a, Cui et al 2018b. Among the dataset, 100 random cases (validation set) were used to determine the number of Pareto optimal plans with the gMCO algorithm, and 462 random cases (test set) were used in the performance evaluation of the gMCO generated plans.…”

Section: Methodsmentioning

confidence: 99%

“…where N O is the number of organs, N pnt,j is the number of dose calculation points in the j th organ. w j is a hidden weight applied to the objectives (surface and volume) of the j th organ to introduce trade-off in the solution space around the population-based starting point as in (Cui et al 2018a, Cui et al 2018b. The hidden weights are always non negative and their sum is one (because of the weighted sum method).…”

Section: Quadratic Objective Function Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Our prior studies (Cui et al 2018a, Cui et al 2018b showed that a patientspecific treatment plan can be created without any user interventions in HDR prostate brachytherapy. However, the optimization engine of these studies was stochastic, and was implemented on CPU hardware (Cui et al 2018a, Cui et al 2018b. As a result, the algorithm inevitably involved an intensive computation (41 s), which may restrain its application in clinical practice, because the patient is under general anesthesia in the operating room waiting for the treatment to be delivered.…”

Section: Introductionmentioning

confidence: 99%

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A GPU-based multi-criteria optimization algorithm for HDR brachytherapy

Bélanger

Cui

et al. 2019

Phys. Med. Biol.

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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.Our results indicated that the optimization process is equivalent between gL-BFGS and gSA, and that the computational performance of gL-BFGS is up to 67 times faster than gSA. 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 V 100 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 multicriteria optimization algorithm was implemented for HDR prostate brachytherapy. Plan pools with various trade-offs can be created with this algorithm. A largescale 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.

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Bi‐objective optimization of catheter positions for high‐dose‐rate prostate brachytherapy

Meer

Bosman

Niatsetski³

et al. 2020

Medical Physics

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Bi-objective simultaneous optimization of catheter positions and dwell times for highdose-rate (HDR) prostate brachytherapy, based directly on dose-volume indices, has shown promising results. However, optimization with the state-of-the-art evolutionary algorithm MO-RV-GOMEA so far required several hours of runtime, and resulting catheter positions were not always clinically feasible. The aim of this study is to extend the optimization model and apply GPU parallelization to achieve clinically acceptable computation times. The resulting optimization procedure is compared with a previously introduced method based solely on geometric criteria, the adapted Centroidal Voronoi Tessellations (CVT) algorithm. Methods: Bi-objective simultaneous optimization was performed with a GPU-parallelized version of MO-RV-GOMEA. This optimization of catheter positions and dwell times was retrospectively applied to the data of 26 patients previously treated with HDR prostate brachytherapy for 8-16 catheters (steps of 2). Optimization of catheter positions using CVT was performed in seconds, after which optimization of only the dwell times using MO-RV-GOMEA was performed in 1 min. Results: Simultaneous optimization of catheter positions and dwell times using MO-RV-GOMEA was performed in 5 min. For 16 down to 8 catheters (steps of 2), MO-RV-GOMEA found plans satisfying the planning-aims for 20, 20, 18, 14, and 11 out of the 26 patients, respectively. CVT achieved this for 19, 17, 13, 9, and 2 patients, respectively. The P-value for the difference between MO-RV-GOMEA and CVT was 0.023 for 16 catheters, 0.005 for 14 catheters, and <0.001 for 12, 10, and 8 catheters. Conclusions: With bi-objective simultaneous optimization on a GPU, high-quality catheter positions can now be obtained within 5 min, which is clinically acceptable, but slower than CVT. For 16 catheters, the difference between MO-RV-GOMEA and CVT is clinically irrelevant. For 14 catheters and less, MO-RV-GOMEA outperforms CVT in finding plans satisfying all planning-aims.

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A multi-criteria optimization approach for HDR prostate brachytherapy: I. Pareto surface approximation

Cited by 16 publications

References 30 publications

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

GPU‐accelerated bi‐objective treatment planning for prostate high‐dose‐rate brachytherapy

A GPU-based multi-criteria optimization algorithm for HDR brachytherapy

Bi‐objective optimization of catheter positions for high‐dose‐rate prostate brachytherapy

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