Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy

Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy

PURPOSE

Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single "optimal" plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for.

METHODS AND MATERIALS

BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols.

RESULTS

Results are obtained for prostate (n = 12) and cervix brachytherapy (n = 36). We demonstrate the possible differences in dose distribution for optimized plans with equal dose-volume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation.

CONCLUSIONS

Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution.

Technical note: Evaluation of a spatial optimization model for prostate high dose-rate brachytherapy in a clinical treatment planning system

BACKGROUND

Spatial properties of a dose distribution, such as volumes of contiguous hot spots, are of clinical importance in treatment planning for high dose-rate brachytherapy (HDR BT). We have in an earlier study developed an optimization model that reduces the prevalence of contiguous hot spots by modifying a tentative treatment plan.

PURPOSE

The aim of this study is to incorporate the correction of hot spots in a standard inverse planning workflow and to validate the integrated model in a clinical treatment planning system. The spatial function is included in the objective function for the inverse planning, as opposed to in the previous study where it was applied as a separate post-processing step. Our aim is to demonstrate that fine-adjustments of dose distributions, which are often performed manually in today's clinical practice, can be automated.

METHODS

A spatial optimization function was introduced in the treatment planning system RayStation (RaySearch Laboratories AB, Stockholm, Sweden) via a research interface. A series of 10 consecutive prostate patients treated with HDR BT was retrospectively replanned with and without the spatial function.

RESULTS

Optimization with the spatial function decreased the volume of the largest contiguous hot spot by on average 31%, compared to if the function was not included. The volume receiving at least 200% of the prescription dose decreased by on average 11%. Target coverage, measured as the fractions of the clinical target volume (CTV) and the planning target volume (PTV) receiving at least the prescription dose, was virtually unchanged (less than a percent change for both metrics). Organs-at-risk received comparable or slightly decreased doses if the spatial function was included in the optimization model.

CONCLUSIONS

Optimization of spatial properties such as the volume of contiguous hot spots can be integrated in a standard inverse planning workflow for brachytherapy, and need not be conducted as a separate post-processing step.

Emerging technologies in brachytherapy

Brachytherapy is a mature treatment modality. The literature is abundant in terms of review articles and comprehensive books on the latest established as well as evolving clinical practices. The intent of this article is to part ways and look beyond the current state-of-the-art and review emerging technologies that are noteworthy and perhaps may drive the future innovations in the field. There are plenty of candidate topics that deserve a deeper look, of course, but with practical limits in this communicative platform, we explore four topics that perhaps is worthwhile to review in detail at this time. First, intensity modulated brachytherapy (IMBT) is reviewed. The IMBT takes advantage ofanisotropicradiation profile generated through intelligent high-density shielding designs incorporated onto sources and applicators such to achieve high quality plans. Second, emerging applications of 3D printing (i.e. additive manufacturing) in brachytherapy are reviewed. With the advent of 3D printing, interest in this technology in brachytherapy has been immense and translation swift due to their potential to tailor applicators and treatments customizable to each individual patient. This is followed by, in third, innovations in treatment planning concerning catheter placement and dwell times where new modelling approaches, solution algorithms, and technological advances are reviewed. And, fourth and lastly, applications of a new machine learning technique, called deep learning, which has the potential to improve and automate all aspects of brachytherapy workflow, are reviewed. We do not expect that all ideas and innovations reviewed in this article will ultimately reach clinic but, nonetheless, this review provides a decent glimpse of what is to come. It would be exciting to monitor as IMBT, 3D printing, novel optimization algorithms, and deep learning technologies evolve over time and translate into pilot testing and sensibly phased clinical trials, and ultimately make a difference for cancer patients. Today's fancy is tomorrow's reality. The future is bright for brachytherapy.

Optimization in treatment planning of high dose-rate brachytherapy - Review and analysis of mathematical models

Treatment planning in high dose-rate brachytherapy has traditionally been conducted with manual forward planning, but inverse planning is today increasingly used in clinical practice. There is a large variety of proposed optimization models and algorithms to model and solve the treatment planning problem. Two major parts of inverse treatment planning for which mathematical optimization can be used are the decisions about catheter placement and dwell time distributions. Both these problems as well as integrated approaches are included in this review. The proposed models include linear penalty models, dose-volume models, mean-tail dose models, quadratic penalty models, radiobiological models, and multiobjective models. The aim of this survey is twofold: (i) to give a broad overview over mathematical optimization models used for treatment planning of brachytherapy and (ii) to provide mathematical analyses and comparisons between models. New technologies for brachytherapy treatments and methods for treatment planning are also discussed. Of particular interest for future research is a thorough comparison between optimization models and algorithms on the same dataset, and clinical validation of proposed optimization approaches with respect to patient outcome.

Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty

PURPOSE

Recently, we introduced a bi-objective optimization approach based on dose-volume indices to automatically create clinically good HDR prostate brachytherapy plans. To calculate dose-volume indices, a reconstruction algorithm is used to determine the 3D organ shape from 2D contours, inevitably containing settings that influence the result. We augment the optimization approach to quickly find plans that are robust to differences in 3D reconstruction.

METHODS

Studied reconstruction settings were: interpolation between delineated organ contours, overlap between contours, and organ shape at the top and bottom contour. Two options for each setting yields 8 possible 3D organ reconstructions per patient, over which the robust model defines minimax optimization. For the original model, settings were based on our treatment planning system. Both models were tested on data of 26 patients and compared by re-evaluating selected optimized plans both in the original model (1 organ reconstruction, the difference determines the cost), and in the robust model (8 organ reconstructions, the difference determines the benefit).

RESULTS

Robust optimization increased the run time from 3 to 6 min. The median cost for robust optimization as observed in the original model was -0.25% in the dose-volume indices with a range of [-0.01%, -1.03%]. The median benefit of robust optimization as observed in the robust model was 0.93% with a range of [0.19%, 4.16%]. For 4 patients, selected plans that appeared good when optimized in the original model, violated the clinical protocol with more than 1% when considering different settings. This was not the case for robustly optimized plans.

CONCLUSIONS

Plans of high quality, irrespective of 3D organ reconstruction settings, can be obtained using our robust optimization approach. With its limited effect on total runtime, our approach therefore offers a way to account for dosimetry uncertainties that result from choices in organ reconstruction settings that is viable in clinical practice.

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

Purpose

Bi‐objective simultaneous optimization of catheter positions and dwell times for high‐dose‐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.