Robust optimization for HDR prostate brachytherapy applied to organ reconstruction uncertainty

Simultaneous catheter and multicriteria optimization for HDR cervical cancer brachytherapy with a complex intracavity/interstitial applicator

BACKGROUND

Complex intracavity and interstitial (IC/IS) applicators, such as the Venezia applicator, can improve the HR-CTV coverage while adequately protecting organs at risk in the treatment of cervical cancer with high-dose-rate (HDR) brachytherapy. Although the Venezia applicator offers more choice for catheter selection, commercially available catheter and dose optimization algorithms are still missing for complex applicators. Moreover, studies on catheter and dose optimization for IC/IS implants in the treatment of cervical cancer are still limited.

PURPOSE

This work aims to combine a GPU-based multi-criteria optimization (gMCO) algorithm with a sparse catheter (SC) optimization algorithm for the Venezia applicator.

METHODS

Fifty-eight cervical cancer patients who received 28 Gy in 4 fx of HDR brachytherapy with the Venezia applicator (combination to external beam radiation therapy) are retrospectively revisited. The modelization of the applicator is done by virtually reconstructing all the IS catheters passing through the ring. Template catheters are reconstructed using an in-house python script. To perform simultaneous MCO and SC optimization (SC+MCO), the objective function includes aggregated dose objectives in a weighted sum and a group sparsity term that individually penalizes the contribution of IS catheters. Plans generated with the SC+MCO algorithm are compared with plans generated with MCO using clinical catheters (CC+MCO) and the clinical plans (CP). The EMBRACE II soft constraints (planning aims) and hard constraints (limits for prescribed dose) are used as plan evaluation criteria.

RESULTS

CC+MCO gives the most important gain with an increase up to 20.7% in meeting all EMBRACE II soft constraints compared with CP. The SC+MCO algorithm (adding catheter optimization to MCO) provides a second order increase (up to 12.1% with total acceptance rate of 60.3% or 35/58) in the acceptance rate versus CC+MCO (total increase of 32.8% vs. CP). Acceptance rate in EMBRACE II hard constraints is 98.3% (57/58) for both CC+MCO and SC+MCO versus 91.4% (53/58) for CP. The median SC+MCO optimization time is 11 s to generate a total of 5000 Pareto-optimal plans with different catheter configurations (position and number) for each fraction.

CONCLUSIONS

Simultaneous catheter and MCO optimization is clinically feasible for HDR cervical cancer brachytherapy using the Venezia applicator. Clinical catheter configurations could be improved and/or the catheter number could be reduced without decreasing plan quality using SC+MCO compared with the CP.

Robust stochastic optimization of needle configurations for robotic HDR prostate brachytherapy

BACKGROUND

Ideally, inverse planning for HDR brachytherapy (BT) should include the pose of the needles which define the trajectory of the source. This would be particularly interesting when considering the additional freedom and accuracy in needle pose which robotic needle placement enables. However, needle insertion typically leads to tissue deformation, resulting in uncertainty regarding the actual pose of the needles with respect to the tissue.

PURPOSE

To efficiently address uncertainty during inverse planning for HDR BT in order to robustly optimize the pose of the needles before insertion, that is, to facilitate path planning for robotic needle placement.

METHODS

We use a form of stochastic linear programming to model the inverse treatment planning problem. To account for uncertainty, we consider random tissue displacements at the needle tip to simulate tissue deformation. Conventionally for stochastic linear programming, each simulated deformation is reflected by an addition to the linear programming problem which increases problem size and computational complexity substantially and leads to impractical runtime. We propose two efficient approaches for stochastic linear programming. First, we consider averaging dose coefficients to reduce the problem size. Second, we study weighting of the slack variables of an adjusted linear problem to approximate the full stochastic linear program. We compare different approaches to optimize the needle configurations and evaluate their robustness with respect to different amounts of tissue deformation.

RESULTS

Our results illustrate that stochastic planning can improve the robustness of the treatment with respect to deformation. The proposed approaches approximating stochastic linear programming better conform to the tissue deformation compared to conventional linear programming. They show good correlation with the plans computed after deformation while reducing the runtime by two orders of magnitude compared to the complete stochastic linear program. Robust optimization of needle configurations takes on average 59.42 s. Skew needle configurations lead to mean coverage improvements compared to parallel needles from 0.39 to 2.94 percentage points, when 8 mm tissue deformation is considered. Considering tissue deformations from 4  to 10 mm during planning with weighted stochastic optimization and skew needles generally results in improved mean coverage from 1.77 to 4.21 percentage points.

CONCLUSIONS

We show that efficient stochastic optimization allows selecting needle configurations which are more robust with respect to potentially negative effects of target deformation and displacement on the achievable prescription dose coverage. The approach facilitates robust path planning for robotic needle placement.

Being certain about uncertainties: a robust evaluation method for high-dose-rate prostate brachytherapy treatment plans including the combination of uncertainties

In high-dose-rate (HDR) prostate brachytherapy the combined effect of uncertainties cause a range of possible dose distributions deviating from the nominal plan, and which are not considered during treatment plan evaluation. This could lead to dosimetric misses for critical structures and overdosing of organs at risk. A robust evaluation method to assess the combination of uncertainties during plan evaluation is presented and demonstrated on one HDR prostate ultrasound treatment plan retrospectively. A range of uncertainty scenarios are simulated by changing six parameters in the nominal plan and calculating the corresponding dose distribution. Two methods are employed to change the parameters, a probabilistic approach using random number sampling to evaluate the likelihood of variation in dose distributions, and a combination of the most extreme possible values to access the worst-case dosimetric outcomes. One thousand probabilistic scenarios were run on the single treatment plan with 43.2% of scenarios passing seven of the eight clinical objectives. The prostate D90 had a standard deviation of 4.4%, with the worst case decreasing the dose by up to 27.2%. The urethra D10 was up to 29.3% higher than planned in the worst case. All DVH metrics in the probabilistic scenarios were found to be within acceptable clinical constraints for the plan under statistical tests for significance. The clinical significance of the results from the robust evaluation method presented on any individual treatment plan needs to be compared in the context of a historical data set that contains patient outcomes with robustness analysis data to ascertain a baseline acceptance.

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.