Comparison of Oncentra® Brachy IPSA and graphical optimisation techniques: a case study of HDR brachytherapy head and neck and prostate plans

AAPM Task Group Report 267: A joint AAPM GEC-ESTRO report on biophysical models and tools for the planning and evaluation of brachytherapy

Brachytherapy utilizes a multitude of radioactive sources and treatment techniques that often exhibit widely different spatial and temporal dose delivery patterns. Biophysical models, capable of modeling the key interacting effects of dose delivery patterns with the underlying cellular processes of the irradiated tissues, can be a potentially useful tool for elucidating the radiobiological effects of complex brachytherapy dose delivery patterns and for comparing their relative clinical effectiveness. While the biophysical models have been used largely in research settings by experts, it has also been used increasingly by clinical medical physicists over the last two decades. A good understanding of the potentials and limitations of the biophysical models and their intended use is critically important in the widespread use of these models. To facilitate meaningful and consistent use of biophysical models in brachytherapy, Task Group 267 (TG-267) was formed jointly with the American Association of Physics in Medicine (AAPM) and The Groupe Européen de Curiethérapie and the European Society for Radiotherapy & Oncology (GEC-ESTRO) to review the existing biophysical models, model parameters, and their use in selected brachytherapy modalities and to develop practice guidelines for clinical medical physicists regarding the selection, use, and interpretation of biophysical models. The report provides an overview of the clinical background and the rationale for the development of biophysical models in radiation oncology and, particularly, in brachytherapy; a summary of the results of literature review of the existing biophysical models that have been used in brachytherapy; a focused discussion of the applications of relevant biophysical models for five selected brachytherapy modalities; and the task group recommendations on the use, reporting, and implementation of biophysical models for brachytherapy treatment planning and evaluation. The report concludes with discussions on the challenges and opportunities in using biophysical models for brachytherapy and with an outlook for future developments.

Modern Tools for Modern Brachytherapy

This review aims to showcase the brachytherapy tools and technologies that have emerged during the last 10 years. Soft-tissue contrast using magnetic resonance and ultrasound imaging has seen enormous growth in use to plan all forms of brachytherapy. The era of image-guided brachytherapy has encouraged the development of advanced applicators and given rise to the growth of individualised 3D printing to achieve reproducible and predictable implants. These advances increase the quality of implants to better direct radiation to target volumes while sparing normal tissue. Applicator reconstruction has moved beyond manual digitising, to drag and drop of three-dimensional applicator models with embedded pre-defined source pathways, ready for auto-recognition and automation. The simplified TG-43 dose calculation formalism directly linked to reference air kerma rate of high-energy sources in the medium water remains clinically robust. Model-based dose calculation algorithms accounting for tissue heterogeneity and applicator material will advance the field of brachytherapy dosimetry to become more clinically accurate. Improved dose-optimising toolkits contribute to the real-time and adaptive planning portfolio that harmonises and expedites the entire image-guided brachytherapy process. Traditional planning strategies remain relevant to validate emerging technologies and should continue to be incorporated in practice, particularly for cervical cancer. Overall, technological developments need commissioning and validation to make the best use of the advanced features by understanding their strengths and limitations. Brachytherapy has become high-tech and modern by respecting tradition and remaining accessible to all.

Planning System for the Optimization of Electric Field Delivery using Implanted Electrodes for Brain Tumor Control

BACKGROUND

The use of non-ionizing electric fields from low intensity voltage sources (<10 V) to control malignant tumor growth is showing increasing potential as a cancer treatment modality. A method of applying these low intensity electric fields using multiple implanted electrodes within or adjacent to tumor volumes has been termed as intratumoral modulation therapy (IMT).

PURPOSE

This study explores advancements in the previously established IMT optimization algorithm, and the development of a custom treatment planning system for patient specific IMT. The practicality of the treatment planning system is demonstrated by implementing the full optimization pipeline on a brain phantom with robotic electrode implantation, post-operative imaging, and treatment stimulation.

METHODS

The integrated planning pipeline in 3D Slicer begins with importing and segmenting patient magnetic resonance images (MRI) or computed tomography (CT) images. The segmentation process is manual, followed by a semi-automatic smoothing step that allows the segmented brain and tumor mesh volumes to be smoothed and simplified by applying selected filters. Electrode trajectories are planned manually on the patient MRI or CT by selecting insertion and tip coordinates for a chosen number of electrodes. The electrode tip positions, and stimulation parameters (phase shift and voltage) can then be optimized with the custom semi-automatic IMT optimization algorithm where users can select the prescription electric field, voltage amplitude limit, tissue electrical properties, nearby organs at risk, optimization parameters (electrode tip location, individual contact phase shift and voltage), desired field coverage percent, and field conformity optimization. Tables of optimization results are displayed, and the resulting electric field is visualized as a field-map superimposed on the MR or CT image, with 3D renderings of the brain, tumor, and electrodes. Optimized electrode coordinates are transferred to robotic electrode implantation software to enable planning and subsequent implantation of the electrodes at the desired trajectories.

RESULTS

An IMT treatment planning system was developed that incorporates patient specific MRI or CT, segmentation, volume smoothing, electrode trajectory planning, electrode tip location and stimulation parameter optimization, and results visualization. All previous manual pipeline steps operating on diverse software platforms were coalesced into a single semi-automated 3D Slicer based user interface. Brain phantom validation of the full system implementation was successful in pre-operative planning, robotic electrode implantation, and post-operative treatment planning to adjust stimulation parameters based on actual implant locations. Voltage measurements were obtained in the brain phantom to determine the electrical parameters of the phantom and validate the simulated electric field distribution.

CONCLUSIONS

A custom treatment planning and implantation system for IMT has been developed in this study, and validated on a phantom brain model, providing an essential step in advancing IMT technology towards future clinical safety and efficacy investigations. This article is protected by copyright. All rights reserved.

Dose optimization comparison study of inverse planning simulated annealing [IPSA] and hybrid inverse planning optimization [HIPO] in interstitial brachytherapy of head and neck cancer

PURPOSE

This study was a retrospective dose optimization comparison of two commercially available inverse planning algorithms, the inverse planning simulated annealing (IPSA) and hybrid inverse planning optimization (HIPO) for head and neck cancer interstitial brachytherapy.

MATERIALS AND METHODS

Seven patients with head and neck cancer were selected (4 with tongue cancer, 2 with buccal mucosa cancer and 1 with carcinoma lip) who were previously treated with interstitial brachytherapy using a flexible nylon tube catheter and graphical optimization/geometric optimization technique. All seven patients were retrospectively re-planned using both IPSA as well as HIPO algorithms available in the Oncentra Brachytherapy Treatment Planning System (TPS) version V4.5.3.30. The dosimetric parameters [PTV-V₁₀₀, V₁₅₀, V₂₀₀, D₉₀; mandible-D2cc, parotid-D2cc, conformity index (CI), dose homogeneity index (HI), overdose volume index (ODI)] were chosen for evaluation in compliance with the objective function and organ at risk dose constraints.

RESULTS

Using the paired sample T test in chosen parameters (PTV-V₁₀₀, V₁₅₀, V₂₀₀, D₉₀; mandible-D2cc, CI, HI, ODI both the inverse planning algorithms), it was found that IPSA and HIPO were comparable.

CONCLUSIONS

Even though both IPSA and HIPO are largely comparable in most of the dosimetric parameters for inverse planning in brachytherapy of head and neck cancers, differences in the algorithms can be exploited to improve certain parameters in specific situations such as D2cc parotid.

Does inverse planning improve plan quality in interstitial high-dose-rate breast brachytherapy?

Purpose

To investigate the effect of input parameters for an inverse optimization algorithm, and dosimetrically evaluate and compare clinical treatment plans made by inverse and forward planning in high-dose-rate interstitial breast implants.

Material and methods

By using a representative breast implant, input parameters responsible for target coverage and dose homogeneity were changed step-by-step, and their optimal values were determined. Then, effects of parameters on dosimetry of normal tissue and organs at risk were investigated. The role of dwell time modulation restriction was also studied. With optimal input parameters, treatment plans of forty-two patients were re-calculated using an inverse optimization algorithm (HIPO). Then, a pair-wise comparison between forward and inverse plans was performed using dose-volume parameters.

Results

To find a compromise between target coverage and dose homogeneity, we recommend using weight factors in the range of 70-90 for minimum dose, and in the range of 10-30 for maximum dose. Maximum dose value of 120% with a weight factor of 5 is recommended for normal tissue. Dose constraints for organs at risk did not play an important role, and the dwell time gradient restriction had only minor effect on target dosimetry. In clinical treatment plans, at identical target coverage, the inverse planning significantly increased the dose conformality (COIN, 0.75 vs. 0.69, p < 0.0001) and improved the homogeneity (DNR, 0.35 vs. 0.39, p = 0.0027), as compared to forward planning. All dosimetric parameters for non-target breast, ipsilateral lung, ribs, and heart were significantly better with inverse planning. The most exposed small volumes for skin were less in HIPO plans, but without statistical significance. Volume irradiated by 5% was 173.5 cm³ in forward and 167.7 cm³ in inverse plans (p = 0.0247).

Conclusions

By using appropriate input parameters, inverse planning can provide dosimetrically superior dose distributions over forward planning in interstitial breast implants.

A new plan quality objective function for determining optimal collimator combinations in prostate cancer treatment with stereotactic body radiation therapy using CyberKnife

Stereotactic body radiation therapy with CyberKnife for prostate cancer has long treatment times compared with conventional radiotherapy. This arises the need for designing treatment plans with short execution times. We propose an objective function for plan quality evaluation, which was used to determine an optimal combination between small and large collimators based on short treatment times and clinically acceptable dose distributions. Data from 11 prostate cancer patients were used. For each patient, 20 plans were created based on all combinations between one small (⌀ 10–25 mm) and one large (⌀ 35–60 mm) Iris collimator size. The objective function was assigned to each combination as a penalty, such that plans with low penalties were considered superior. This function considered the achievement of dosimetric planning goals, tumor control probability, normal tissue complication probability, relative seriality parameter, and treatment time. Two methods were used to determine the optimal combination. First, we constructed heat maps representing the mean penalty values and standard deviations of the plans created for each collimator combination. The combination giving a plan with the smallest mean penalty and standard deviation was considered optimal. Second, we created two groups of superior plans: group A plans were selected by histogram analysis and group B plans were selected by choosing the plan with the lowest penalty from each patient. In both groups, the most used small and large collimators were assumed to represent the optimal combination. The optimal combinations obtained from the heat maps included the 25 mm as a small collimator, giving small/large collimator sizes of 25/35, 25/40, 25/50, and 25/60 mm. The superior-group analysis indicated that 25/50 mm was the optimal combination. The optimal Iris combination for prostate cancer treatment using CyberKnife was determined to be a collimator size between 25 mm (small) and 50 mm (large).