Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent

Automated treatment planning framework for brachytherapy of cervical cancer using 3D dose predictions

Objective. To lay the foundation for automated knowledge-based brachytherapy treatment planning using 3D dose estimations, we describe an optimization framework to convert brachytherapy dose distributions directly into dwell times (DTs).Approach. A dose rate kernelḋ(r,θ,φ)was produced by exporting 3D dose for one dwell position from the treatment planning system and normalizing by DT. By translating and rotating this kernel to each dwell position, scaling by DT and summing over all dwell positions, dose was computed (Dcalc). We used a Python-coded COBYLA optimizer to iteratively determine the DTs that minimize the mean squared error betweenDcalcand reference doseDref, computed using voxels withDref80%-120% of prescription. As validation of the optimization, we showed that the optimizer replicates clinical plans whenDref= clinical dose in 40 patients treated with tandem-and-ovoid (T&O) or tandem-and-ring (T&R) and 0-3 needles. Then we demonstrated automated planning in 10 T&O usingDref= dose predicted from a convolutional neural network developed in past work. Validation and automated plans were compared to clinical plans using mean absolute differences (MAD=1N∑n=1Nabsxn-xn') over all voxels (xn= Dose,N= #voxels) and DTs (xn= DT,N= #dwell positions), mean differences (MD) in organD2ccand high-risk CTV D90 over all patients (where positive indicates higher clinical dose), and mean Dice similarity coefficients (DSC) for 100% isodose contours.Main results. Validation plans agreed well with clinical plans (MADdose= 1.1%, MADDT= 4 s or 0.8% of total plan time,D2ccMD = -0.2% to 0.2% and D90 MD = -0.6%, DSC = 0.99). For automated plans, MADdose= 6.5% and MADDT= 10.3 s (2.1%). The slightly higher clinical metrics in automated plans (D2ccMD = -3.8% to 1.3% and D90 MD = -5.1%) were due to higher neural network dose predictions. The overall shape of the automated dose distributions were similar to clinical doses (DSC = 0.91).Significance. Automated planning with 3D dose predictions could provide significant time savings and standardize treatment planning across practitioners, regardless of experience.

Optimization of isocenter position for multiple targets with nonuniform-margin expansion

PURPOSE

The single isocenter for multiple-target (SIMT) technique has become a popular treatment technique for multiple brain metastases. We have implemented a method to obtain a nonuniform margin for SIMT technique. In this study, we further propose a method to determine the isocenter position so that the total expanded margin volume is minimal.

MATERIALS AND METHOD

Based on a statistical model, the relationship between nonuniform margin and the distance d (from isocenter to target point), setup uncertainties, and significance level was established. Due to the existence of rotational error, there is a nonlinear relationship between the margin volume and the isocenter position. Using numerical simulation, we study the relationship between optimal isocenter position and translational error, rotational error, and target size. In order to find the optimal isocenter position quickly, adaptive simulated annealing (ASA) algorithm was used. This method was implemented in the Pinnacle3 treatment planning system and compared with isocenter at center-of-geometric (COG), center-of-volume (COV), and center-of-surface (COS). Ten patients with multiple brain metastasis targets treated with the SIMT technique was selected for evaluation.

RESULTS

When the size of tumors is equal, the optimal isocenter obtained by ASA and numerical simulation coincides with COG, COV, and COS. When the size of tumors is different, the optimal isocenter is close to the large tumor. The position of COS point is closer to the optimal point than the COV point for nearly all cases. Moreover, in some cases the COS point can be approximately selected as the optimal point. The ASA algorithm can reduce the calculating time from several hours to tens of seconds for three or more tumors. Using multiple brain metastases targets, a series of volume difference and calculating time were obtained for various tumor number, tumor size, and separation distances. Compared with the margin volume with isocenter at COG, the margin volume for optimal point can be reduced by up to 27.7%.

CONCLUSION

Optimal treatment isocenter selection of multiple targets with large differences could reduce the total margin volume. ASA algorithm can significantly improve the speed of finding the optimal isocenter. This method can be used for clinical isocenter selection and is useful for the protection of normal tissue nearby.

Distributed and scalable optimization for robust proton treatment planning

BACKGROUND

The importance of robust proton treatment planning to mitigate the impact of uncertainty is well understood. However, its computational cost grows with the number of uncertainty scenarios, prolonging the treatment planning process.

PURPOSE

We developed a fast and scalable distributed optimization platform that parallelizes the robust proton treatment plan computation over the uncertainty scenarios.

METHODS

We modeled the robust proton treatment planning problem as a weighted least-squares problem. To solve it, we employed an optimization technique called the alternating direction method of multipliers with Barzilai-Borwein step size (ADMM-BB). We reformulated the problem in such a way as to split the main problem into smaller subproblems, one for each proton therapy uncertainty scenario. The subproblems can be solved in parallel, allowing the computational load to be distributed across multiple processors (e.g., CPU threads/cores). We evaluated ADMM-BB on four head-and-neck proton therapy patients, each with 13 scenarios accounting for 3 mm setup and 3.5% range uncertainties. We then compared the performance of ADMM-BB with projected gradient descent (PGD) applied to the same problem.

RESULTS

For each patient, ADMM-BB generated a robust proton treatment plan that satisfied all clinical criteria with comparable or better dosimetric quality than the plan generated by PGD. However, ADMM-BB's total runtime averaged about 6 to 7 times faster. This speedup increased with the number of scenarios.

CONCLUSIONS

ADMM-BB is a powerful distributed optimization method that leverages parallel processing platforms, such as multicore CPUs, GPUs, and cloud servers, to accelerate the computationally intensive work of robust proton treatment planning. This results in (1) a shorter treatment planning process and (2) the ability to consider more uncertainty scenarios, which improves plan quality.

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.

Automatic optimization of treatment dosimetry to improve visual outcomes in episcleral plaque brachytherapy

PURPOSE

The aim of this study was to develop an automated dose optimization algorithm, powered by simulated annealing, for inverse planning of ocular plaque brachytherapy treatments, to improve tumor coverage and critical structure sparing for reduced treatment-related morbidity.

METHODS AND MATERIALS

A simulated annealing based algorithm was built and evaluated by finding variable seed strengths that maximized dose uniformity across tumor base for model cases of various tumor heights and plaque sizes. This problem assessed its potential to find the minima in the energy function using differential loading. The algorithm was further developed to decrease doses to critical structures while maintaining desired tumor coverage. Doses to the optic disc and fovea were compared to those using uniform seed strengths for various model cases. Finally, 10 retrospective patient cases treated with uniform seed strengths were replanned with the developed algorithm to evaluate effectiveness for clinical application.

RESULTS

The developed algorithm achieved dose reductions of up to 37.3% and 39.6% to the optic disc and fovea, respectively, compared to those using uniform seed strengths, depending on the tumor and plaque size used for the model cases. Applying the algorithm to 10 clinical scenarios resulted in dose reductions of 14% to the optic disc and fovea relative to clinical treatments performed with uniform seed strengths.

CONCLUSIONS

The developed automatic dose optimization routine was able to achieve significant dose reductions to the critical structures relative to using uniformly loaded plaques both in the model and in the clinical cases.

Patient's specific integration of OAR doses (D2 cc) from EBRT and 3D image-guided brachytherapy for cervical cancer

The objective of this study was to assess the recommended DVH parameter (e.g., D2 cc) addition method used for combining EBRT and HDR plans, against a reference dataset generated from an EQD2‐based DVH addition method. A revised DVH parameter addition method using EBRT DVH parameters derived from each patient's plan was proposed and also compared with the reference dataset. Thirty‐one biopsy‐proven cervical cancer patients who received EBRT and HDR brachytherapy were retrospectively analyzed. A parametrial and/or paraaortic EBRT boost were clinically performed on 13 patients. Ten IMRT and 21 3DCRT plans were determined. Two different HDR techniques for each HDR plan were analyzed. Overall D2 cc and D0.1 cc OAR doses in EQD2 were statistically analyzed for three different DVH parameter addition methods: a currently recommended method, a proposed revised method, and a reference DVH addition method. The overall D2 cc_EQD2 values for all rectum, bladder, and sigmoid for a conformal, volume optimization HDR plan generated using the current DVH parameter addition method were significantly underestimated on average −5 to −8% when compared to the values obtained from the reference DVH addition technique (P < 0.01). The revised DVH parameter addition method did not present statistical differences with the reference technique (P > 0.099). When PM boosts were considered, there was an even greater average underestimation of −8~−10% for overall OAR doses of conformal HDR plans when using the current DVH parameter addition technique as compared to the revised DVH parameter addition. No statistically significant differences were found between the 3DCRT and IMRT techniques (P > 0.3148). It is recommended that the overall D2 cc EBRT doses are obtained from each patient's EBRT plan.