A new optimization algorithm for HDR brachytherapy that improves DVH-based planning: Truncated Conditional Value-at-Risk (TCVaR)

Influence of optimisation parameters on directly deliverable Pareto fronts explored for prostate cancer

PURPOSE

In inverse radiotherapy treatment planning, the Pareto front is the set of optimal solutions to the multi-criteria problem of adequately irradiating the planning target volume (PTV) while reducing dose to organs at risk (OAR). The Pareto front depends on the chosen optimisation parameters whose influence (clinically relevant versus not clinically relevant) is investigated in this paper.

METHODS

Thirty-one prostate cancer patients treated at our clinic were randomly selected. We developed an in-house Python script that controlled the commercial treatment planning system RayStation to calculate directly deliverable Pareto fronts. We calculated reference Pareto fronts for a given set of objective functions, varying the PTV coverage and the mean dose of the primary OAR (rectum) and fixing the mean doses of the secondary OARs (bladder and femoral heads). We calculated the fronts for different sets of objective functions and different mean doses to secondary OARs. We compared all fronts using a specific metric (clinical distance measure).

RESULTS

The in-house script was validated for directly deliverable Pareto front calculations in two and three dimensions. The Pareto fronts depended on the choice of objective functions and fixed mean doses to secondary OARs, whereas the parameters most influencing the front and leading to clinically relevant differences were the dose gradient around the PTV, the weight of the PTV objective function, and the bladder mean dose.

CONCLUSIONS

Our study suggests that for multi-criteria optimisation of prostate treatments using external therapy, dose gradient around the PTV and bladder mean dose are the most influencial parameters.

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.