ResearchPad - therapeutic-interventions https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Dosimetric accuracy of delivering SBRT using dynamic arcs on Cyberknife]]> https://www.researchpad.co/article/elastic_article_6902 Several studies have demonstrated potential improvements in treatment time through the use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife. However, the delivery system has a finite accuracy, so that potential exists for dosimetric uncertainties. This study estimates the expected dosimetric accuracy of dynamic delivery of SBRT, based on realistic estimates of the uncertainties in delivery parameters.MethodsFive SBRT patient cases (prostate A — conventional, prostate B — brachytherapy‐type, lung, liver, partial left breast) were retrospectively studied. Treatment plans were produced for a fixed arc trajectory using fluence optimization, segmentation, and direct aperture optimization. Dose rate uncertainty was modeled as a smoothly varying random fluctuation of ± 1.0%, ±2.0% or ± 5.0% over a time period of 10, 30 or 60 s. Multileaf collimator uncertainty was modeled as a lag in position of each leaf up to 0.25 or 0.5 mm. Robot pointing error was modeled as a shift of the target location, with the direction of the shift chosen as a random angle with respect to the multileaf collimator and with a random magnitude in the range 0.0–1.0 mm at the delivery nodes and with an additional random magnitude of 0.5–1.0 mm in between the delivery nodes. The impact of the errors was investigated using dose‐volume histograms.ResultsUncertainty in dose rate has the effect of varying the total monitor units delivered, which in turn produces a variation in mean dose to the planning target volume. The random sampling of dose rate error produces a distribution of mean doses with a standard deviation proportional to the magnitude of the dose rate uncertainty. A lag in multileaf collimator position of 0.25 or 0.5 mm produces a small impact on the delivered dose. In general, an increase in the PTV mean dose of around 1% is observed. An error in robot pointing of the order of 1 mm produces a small increase in dose inhomogeneity to the planning target volume, sometimes accompanied by an increase in mean dose by around 1%.ConclusionsBased upon the limited data available on the dose rate stability and geometric accuracy of the Cyberknife system, this study estimates that dynamic arc delivery can be accomplished with sufficient accuracy for clinical application. Dose rate variation produces a change in dose to the planning target volume according to the perturbation of total monitor units delivered, while multileaf collimator lag and robot pointing error typically increase the mean dose to the planning target volume by up to 1%. ]]> <![CDATA[Development of a CT number calibration audit phantom in photon radiation therapy: A pilot study]]> https://www.researchpad.co/article/elastic_article_6761 In photon radiation therapy, computed tomography (CT) numbers are converted into values for mass density (MD) or relative electron density to water (RED). CT‐MD or CT‐RED calibration tables are relevant for human body dose calculation in an inhomogeneous medium. CT‐MD or CT‐RED calibration tables are influenced by patient imaging (CT scanner manufacturer, scanning parameters, and patient size), the calibration process (tissue‐equivalent phantom manufacturer, and selection of tissue‐equivalent material), differences between tissue‐equivalent materials and standard tissues, and the dose calculation algorithm applied; however, a CT number calibration audit has not been established. The purposes of this study were to develop a postal audit phantom, and to establish a CT number calibration audit process.MethodsA conventional stoichiometric calibration conducts a least square fit of the relationships between the MD, material weight, and measured CT number, using two parameters. In this study, a new stoichiometric CT number calibration scheme has been empirically established, using three parameters to harmonize the calculated CT number with the measured CT number for air and lung tissue. In addition, the suitable material set and the minimal number of materials required for stoichiometric CT number calibration were determined. The MDs and elemental weights from the International Commission on Radiological Protection Publication 110 were used as standard tissue data, to generate the CT‐MD and CT‐RED calibration tables. A small‐sized, CT number calibration phantom was developed for a postal audit, and stoichiometric CT number calibration with the phantom was compared to the CT number calibration tables registered in the radiotherapy treatment planning systems (RTPSs) associated with five radiotherapy institutions.ResultsWhen a least square fit was performed for the stoichiometric CT number calibration with the three parameters, the calculated CT number showed better agreement with the measured CT number. We established stoichiometric CT number calibration using only two materials because the accuracy of the process was determined not by the number of used materials but by the number of elements contained. The stoichiometric CT number calibration was comparable to the tissue‐substitute calibration, with a dose difference less than 1%. An outline of the CT number calibration audit was demonstrated through a multi‐institutional study.ConclusionsWe established a new stoichiometric CT number calibration method for validating the CT number calibration tables registered in RTPSs. We also developed a CT number calibration phantom for a postal audit, which was verified by the performances of multiple CT scanners located at several institutions. The new stoichiometric CT number calibration has the advantages of being performed using only two materials, and decreasing the difference between the calculated and measured CT numbers for air and lung tissue. In the future, a postal CT number calibration audit might be achievable using a smaller phantom. ]]> <![CDATA[Automatic configuration of the reference point method for fully automated multi‐objective treatment planning applied to oropharyngeal cancer]]> https://www.researchpad.co/article/elastic_article_6758 In automated treatment planning, configuration of the underlying algorithm to generate high‐quality plans for all patients of a particular tumor type can be a major challenge. Often, a time‐consuming trial‐and‐error tuning procedure is required. The purpose of this paper is to automatically configure an automated treatment planning algorithm for oropharyngeal cancer patients.MethodsRecently, we proposed a new procedure to automatically configure the reference point method (RPM), a fast automatic multi‐objective treatment planning algorithm. With a well‐tuned configuration, the RPM generates a single Pareto optimal treatment plan with clinically favorable trade‐offs for each patient. The automatic configuration of the RPM requires a set of computed tomography (CT) scans with corresponding dose distributions for training. Previously, we demonstrated for prostate cancer planning with 12 objectives that training with only 9 patients resulted in high‐quality configurations. This paper further develops and explores the new automatic RPM configuration procedure for head and neck cancer planning with 22 objectives. Investigations were performed with planning CT scans of 105 previously treated unilateral or bilateral oropharyngeal cancer patients together with corresponding Pareto optimal treatment plans. These plans were generated with our clinically applied two‐phase ε‐constraint method (Erasmus‐iCycle) for automated multi‐objective treatment planning, ensuring consistent high quality and Pareto optimality of all plans. Clinically relevant, nonconvex criteria, such as dose‐volume parameters and NTCPs, were included to steer the RPM configuration.ResultsTraining sets with 20–50 patients were investigated. Even with 20 training plans, high‐quality configurations of the RPM were feasible. Automated plan generation with the automatically configured RPM resulted in Pareto optimal plans with overall similar or better quality than that of the Pareto optimal database plans.ConclusionsAutomatic configuration of the RPM for automated treatment planning is feasible and drastically reduces the time and workload required when compared to manual tuning of an automated treatment planning algorithm. ]]> <![CDATA[Treatment planning optimization with beam motion modeling for dynamic arc delivery of SBRT using Cyberknife with multileaf collimation]]> https://www.researchpad.co/article/Nd330b7d3-9516-4025-a5b7-f20cf3172056

Purpose

The use of dynamic arcs for delivery of stereotactic body radiation therapy (SBRT) on Cyberknife is investigated, with a view to improving treatment times. This study investigates the required modeling of robot and multileaf collimator (MLC) motion between control points in the trajectory and then uses this to develop an optimization method for treatment planning of a dynamic arc with Cyberknife. The resulting plans are compared in terms of dose‐volume histograms and estimated treatment times with those produced by a conventional beam arrangement.

Methods

Five SBRT patient cases (prostate A — conventional, prostate B — brachytherapy‐type, lung, liver, and partial left breast) were retrospectively studied. A suitable arc trajectory with control points spaced at 5° was proposed and treatment plans were produced for typical clinical protocols. The optimization consisted of a fluence optimization, segmentation, and direct aperture optimization using a gradient descent method. Dose delivered by the moving MLC was either taken to be the dose delivered discretely at the control points or modeled using effective fluence delivered between control points. The accuracy of calculated dose was assessed by recalculating after optimization using five interpolated beams and 100 interpolated apertures between each optimization control point. The resulting plans were compared using dose‐volume histograms and estimated treatment times with those for a conventional Cyberknife beam arrangement.

Results

If optimization is performed based on discrete doses delivered at the arc control points, large differences of up to 40% of the prescribed dose are seen when recalculating with interpolation. When the effective fluence between control points is taken into account during optimization, dosimetric differences are <2% for most structures when the plans are recalculated using intermediate nodes, but there are differences of up to 15% peripherally. Treatment plan quality is comparable between the arc trajectory and conventional body path. All plans meet the relevant clinical goals, with the exception of specific structures which overlap with the planning target volume. Median estimated treatment time is 355 s (range 235–672 s) for arc delivery and 675 s (range 554–1025 s) for conventional delivery.

Conclusions

The method of using effective fluence to model MLC motion between control points is sufficiently accurate to provide for accurate inverse planning of dynamic arcs with Cyberknife. The proposed arcing method produces treatment plans with comparable quality to the body path, with reduced estimated treatment delivery time.

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<![CDATA[An extended dose–volume model in high dose‐rate brachytherapy – Using mean‐tail‐dose to reduce tumor underdosage]]> https://www.researchpad.co/article/N28ce0472-4234-4538-857c-2bd084ad1253

Purpose

High dose–rate brachytherapy is a method of radiotherapy for cancer treatment in which the radiation source is placed within the body. In addition to give a high enough dose to a tumor, it is also important to spare nearby healthy organs [organs at risk (OAR)]. Dose plans are commonly evaluated using the so‐called dosimetric indices; for the tumor, the portion of the structure that receives a sufficiently high dose is calculated, while for OAR it is instead the portion of the structure that receives a sufficiently low dose that is of interest. Models that include dosimetric indices are referred to as dose–volume models (DVMs) and have received much interest recently. Such models do not take the dose to the coldest (least irradiated) volume of the tumor into account, which is a distinct weakness since research indicates that the treatment effect can be largely impaired by tumor underdosage even to small volumes. Therefore, our aim is to extend a DVM to also consider the dose to the coldest volume.

Methods

An improved DVM for dose planning is proposed. In addition to optimizing with respect to dosimetric indices, this model also takes mean dose to the coldest volume of the tumor into account.

Results

Our extended model has been evaluated against a standard DVM in ten prostate geometries. Our results show that the dose to the coldest volume could be increased, while also computing times for the dose planning were improved.

Conclusion

While the proposed model yields dose plans similar to other models in most aspects, it fulfils its purpose of increasing the dose to cold tumor volumes. An additional benefit is shorter solution times, and especially for clinically relevant times (of minutes) we show major improvements in tumour dosimetric indices.

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<![CDATA[Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer]]> https://www.researchpad.co/article/Nb6101ff4-a5e8-4406-b07c-cbc6612d49eb

Purpose

To develop and validate a robust and accurate registration pipeline for automatic contour propagation for online adaptive Intensity‐Modulated Proton Therapy (IMPT) of prostate cancer using elastix software and deep learning.

Methods

A three‐dimensional (3D) Convolutional Neural Network was trained for automatic bladder segmentation of the computed tomography (CT) scans. The automatic bladder segmentation alongside the computed tomography (CT) scan is jointly optimized to add explicit knowledge about the underlying anatomy to the registration algorithm. We included three datasets from different institutes and CT manufacturers. The first was used for training and testing the ConvNet, where the second and the third were used for evaluation of the proposed pipeline. The system performance was quantified geometrically using the dice similarity coefficient (DSC), the mean surface distance (MSD), and the 95% Hausdorff distance (HD). The propagated contours were validated clinically through generating the associated IMPT plans and compare it with the IMPT plans based on the manual delineations. Propagated contours were considered clinically acceptable if their treatment plans met the dosimetric coverage constraints on the manual contours.

Results

The bladder segmentation network achieved a DSC of 88% and 82% on the test datasets. The proposed registration pipeline achieved a MSD of 1.29 ± 0.39, 1.48 ± 1.16, and 1.49 ± 0.44 mm for the prostate, seminal vesicles, and lymph nodes, respectively, on the second dataset and a MSD of 2.31 ± 1.92 and 1.76 ± 1.39 mm for the prostate and seminal vesicles on the third dataset. The automatically propagated contours met the dose coverage constraints in 86%, 91%, and 99% of the cases for the prostate, seminal vesicles, and lymph nodes, respectively. A Conservative Success Rate (CSR) of 80% was obtained, compared to 65% when only using intensity‐based registration.

Conclusion

The proposed registration pipeline obtained highly promising results for generating treatment plans adapted to the daily anatomy. With 80% of the automatically generated treatment plans directly usable without manual correction, a substantial improvement in system robustness was reached compared to a previous approach. The proposed method therefore facilitates more precise proton therapy of prostate cancer, potentially leading to fewer treatment‐related adverse side effects.

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