Development of patient and catheter specific error thresholds for high dose rate prostate brachytherapy.

BACKGROUND: In-vivo source tracking has been an active topic of research in the field of high-dose rate brachytherapy in recent years to verify accuracy in treatment delivery. Although detection systems for source tracking are being developed, the allowable threshold of treatment error is still unknown and is likely patient-specific due to anatomy and planning variation. PURPOSE: The purpose of this study was to determine patient and catheter-specific shift error thresholds for in-vivo source tracking during high-dose-rate prostate brachytherapy (HDRPBT). METHODS: A module was developed in the previously described graphical processor unit multi-criteria optimization (gMCO) algorithm. The module generates systematic catheter shift errors retrospectively into HDRPBT treatment plans, performed on 50 patients. The catheter shift model iterates through the number of catheters shifted in the plan (from 1 to all catheters), the direction of shift (superior, inferior, medial, lateral, cranial, and caudal), and the magnitude of catheter shift (1-6 mm). For each combination of these parameters, 200 error plans were generated, randomly selecting the catheters in the plan to shift. After shifts were applied, dose volume histogram (DVH) parameters were re-calculated. Catheter shift thresholds were then derived based on plans where DVH parameters were clinically unacceptable (prostate V100 < 95%, urethra D0.1cc > 118%, and rectum Dmax > 80%). Catheter thresholds were also Pearson correlated to catheter robustness values. RESULTS: Patient-specific thresholds varied between 1 to 6 mm for all organs, in all shift directions. Overall, patient-specific thresholds typically decrease with an increasing number of catheters shifted. Anterior and inferior directions were less sensitive than other directions. Pearson's correlation test showed a strong correlation between catheter robustness and catheter thresholds for the rectum and urethra, with correlation values of -0.81 and -0.74, respectively (p < 0.01), but no correlation was found for the prostate. CONCLUSIONS: It was possible to determine thresholds for each patient, with thresholds showing dependence on shift direction, and number of catheters shifted. Not every catheter combination is explorable, however, this study shows the feasibility to determine patient-specific thresholds for clinical application. The correlation of patient-specific thresholds with the equivalent robustness value indicated the need for robustness consideration during plan optimization and treatment planning.

Establishing a fingerprinting method for fast catheter identification in HDR brachytherapy in vivo dosimetry.

PURPOSE: To use quantities measurable during in vivo dosimetry to build unique channel identifiers, that enable detection of brachytherapy errors. MATERIALS AND METHODS: Treatment plan of 360 patients with prostate cancer who underwent high-dose-rate brachytherapy (range, 16-25 catheters; mean, 17) were used. A single point virtual dosimeter was placed at multiple positions within the treatment geometry, and the source-dosimeter distance and dwell time were determined for each dwell position in each catheter. These values were compared across all catheters, dwell position by dwell position, simulating a treatment delivery. A catheter was considered uniquely identified if, for a given dwell position, no other catheters had the same measured values. The minimum number of dwell positions needed to identify a specific catheter and the optimal dosimeter location uniquely were determined. The radial (r) and vertical (z) dimensions of the source-dosimeter distance were also examined for their utility in discriminating catheters. RESULTS: Using a virtual dosimeter with no uncertainties, all catheters were identified in 359 of the 360 cases with 9 dwell position measurements. When only the dwell time were measured, all catheters were uniquely identified after 1 dwell position. With a 2-mm spatial accuracy (r,z), all catheters were identified in 94% of the plans. Simultaneous measurement of source-dosimeter distance and dwell time ensured full catheter identification in all plans ranging from 2 to 6 dwell positions. The number of dwell positions needed to uniquely identify all catheters was lower when the distance from the implant center was higher. CONCLUSIONS: The most efficient fingerprinting approach involved combining source-dosimeter distance (i.e., source tracking) and dwell time. The further the dosimeter is placed from the center of the implant the better it can uniquely identify catheters.

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.

Commissioning of GPU-based multi-criteria optimizer combined with plan navigation tools for high-dose-rate brachytherapy.

Purpose: Recently, our GPU-based multi-criteria optimization (gMCO) algorithm has been integrated in a graphical user interface (gMCO-GUI) that allows real-time plan navigation through a gMCO-generated set of Pareto-optimal plans for high-dose-rate (HDR) brachytherapy. This work reports on the commissioning of the gMCO algorithm into clinical workflow. Material and methods: Our MCO workflow was validated against Oncentra Prostate v. 4.2.2 (OcP) and Oncentra Brachy v. 4.6.0 (OcB). 40 HDR prostate brachytherapy patients (20 with OcP and 20 with OcB) were retrospectively re-planned with gMCO algorithm by generating 2,000 Pareto-optimal plans. A single gMCO treatment plan was exported using gMCO-GUI plan navigation tools. The optimized dwell positions and dwell times of gMCO plans were exported via DICOM RTPLAN files to OcP/OcB, where final dosimetry was calculated. TG43 implementation in gMCO was validated against the consensus data of flexisource. Five analytical shapes were used as the ground truth for volume calculations. Dose-volume histogram (DVH) curves generated by gMCO were compared with the ones generated by OcP/OcB. 3D dose distributions (and isodose lines) were validated against OcP/OcB using dice similarity coefficient (DSC), 95% undirected Hausdorff distance (95% HD), and γ analysis. Results: Differences between -0.4% and 0.3% were observed between gMCO calculated dose rates and the flexisource consensus data. gMCO volumes were within ±2% agreement in 3/5 volumes (deviations within -2.9% and 0.1%). For 9 key DVH indices, the differences between gMCO and OcP/OcB were within ±1.2%. Regarding the accuracy of key isodose lines, the mean DSC was greater than 0.98, and the mean 95% HD was below 0.4 mm. The fraction of voxels with γ ≤ 1 was greater than 99% for all cases with 1%/1 mm threshold. Conclusions: The GPU-based MCO workflow was successfully integrated into the clinical workflow and validated against OcP and OcB.

Catheters and dose optimization using a modified CVT algorithm and multi-criteria optimization in prostate HDR brachytherapy.

PURPOSE: Currently, in high-dose rate (HDR) brachytherapy planning, the catheter's positions are often selected by the planner, which involves the planner's experience. The catheters are then inserted using a template that helps to guide the catheters. For certain applications, it is of interest to choose the optimal location and number of catheters needed for dose coverage and potential decrease of the treatment's toxicity. Hence, it is of great importance to develop patient-specific algorithms for catheters and dose optimization. METHODS: A modified Centroidal Voronoi tessellation (CVT) algorithm is implemented and merged with a graphics processing unit (GPU)-based multi-criteria optimization algorithm (gMCO). The CVT algorithm optimizes the catheters' positions, and the gMCO algorithm optimizes the dwell times and dwell positions. The CVT algorithm can be used simultaneously for insertion with or without a template. Some improvements to the CVT algorithm are presented such as a new way of considering the area that needs to be covered. One hundred eight previously treated prostates HDR cases using real-time ultrasound are used to evaluate the different optimization procedures. The plan robustness is evaluated using two types of errors: deviations (random) in the insertion and deviation (systematic) in the reconstruction of the catheters. RESULTS: Using gMCO on clinically inserted catheter increases the acceptance rate by 37% for Radiation Therapy Oncology Group (RTOG) criteria. Our results show that all the patients respect RTOG criteria with 11 catheters using CVT+gMCO with a template of 5 mm. The number of catheters needed for all patients to respect RTOG criteria with the freehand technique is 10 catheters using CVT+gMCO. When deviations are introduced, using a template, the acceptance rate goes to 85% with 3 mm deviations using 11 catheters. This decrease is less significant when the number of catheters is higher, decreasing by less than 5% with a 3 mm deviation using 13 catheters or more. In conclusion, it is feasible to decrease the number of catheters needed to treat most patients. CONCLUSIONS: Some cases still need a high number of catheters to reach the plan's criteria. Using gMCO allows an increase in the plan quality, while using CVT reduces the number of catheters. A higher number of catheters equates to plans that are more robust to deviations.

Inter-observer evaluation of a GPU-based multicriteria optimization algorithm combined with plan navigation tools for HDR brachytherapy.

PURPOSE: Recently, a GPU-based multicriteria optimization (gMCO) algorithm was integrated in a graphical user interface (gMCO-GUI) that allowed real-time plan navigation through a set of Pareto-optimal plans for high-dose-rate (HDR) brachytherapy. This work reports on the inter-observer evaluation of the gMCO algorithm into the clinical workflow. METHODS AND MATERIALS: Twenty HDR brachytherapy prostate cancer patients were retrospectively replanned with the gMCO algorithm. The reference clinical plans were each generated by experienced physicists using inverse planning followed by graphical optimization and approved by a radiation oncologist (RO). Each case was replanned with the gMCO algorithm by generating 2000 Pareto-optimal plans with four different objective functions. Two physicists were asked to rank the objective functions according to their preferences by choosing one preferred plan for each plans pool and ranking them using gMCO-GUI. The optimized dwell positions and dwell times of the gMCO plans that were ranked first were exported to Oncentra Prostate where a blinded comparison of the gMCO plans with the clinical plans was conducted by three ROs. RESULTS: The median planning time of the two physicists was 9 min. Both physicists preferred the objective function with target sub-regions to cover specific target regions. Regarding the blinded comparison, the gMCO plans were preferred 19, 17, and 12 times by the three ROs, in which eight gMCO plans were unanimously preferred compared with the clinical plans. CONCLUSIONS: The plan quality and the planning time were similar between the two physicists and within what is observed in the clinic. Moreover, the gMCO plans evaluated favorably by ROs compared to the reference clinical plans.

Evaluating the impact of real-time multicriteria optimizers integrated with interactive plan navigation tools for HDR brachytherapy.

PURPOSE: Currently in high-dose-rate (HDR) brachytherapy planning, manual fine-tuning of an objective function is a common practice. Furthermore, automated planning approaches such as multicriteria optimization (MCO) are still limited to the automatic generation of a single treatment plan. This study aims to quantify planning efficiency gains when using a graphics processing unit-based MCO (gMCO) algorithm combined with a novel graphical user interface (gMCO-GUI) that integrates efficient automated and interactive plan navigation tools. METHODS AND MATERIALS: The gMCO algorithm was used to generate 1000 Pareto optimal plans per case for 379 prostate cases. gMCO-GUI was developed to allow plan navigation through all plans. gMCO-GUI integrates interactive parameter selection tools directly with the optimization algorithm to allow plan navigation. The quality of each plan was evaluated based on the Radiation Treatment Oncology Group 0924 protocol and a more stringent institutional protocol (INSTp). gMCO-GUI allows real-time time display of the dose-volume histogram indices, the dose-volume histogram curves, and the isodose lines during the plan navigation. RESULTS: Over the 379 cases, the fraction of Radiation Treatment Oncology Group 0924 protocol valid plans with target coverage greater than 95% was 90.8%, compared with 66.0% for clinical plans. The fraction of INSTp valid plans with target coverage greater than 95% was 81.8%, compared with 62.3% for clinical plans. The average time to compute 1000 deliverable plans with gMCO was 12.5 s, including the full computation of the 3D dose distributions. CONCLUSIONS: Combining the gMCO algorithm with automated and interactive plan navigation tools resulted in simultaneous gains in both plan quality and planning efficiency.

Brachytherapy Future Directions.

Brachytherapy has advanced dramatically in the last decade due largely to improvements in applicators, imaging, treatment planning, and use of clinical trials. In addition, current research in brachytherapy technology continues to change how we deliver this treatment modality. The future of brachytherapy lies in the ability of new technologies to overcome real or perceived barriers. The focus for this manuscript is on specific tools that have or are near to being introduced in the clinic. First, we explore the impact electromagnetic tracking technologies can have on brachytherapy implants and planning workflow. This is followed by an overview of the use of 3D printing and its ability to help tailor brachytherapy implants. Next, we discuss advances in self-shielded applicators and intensity-modulated brachytherapy technology. The manuscript closes out with 2 sections on treatment planning. First is a discussion of biological optimization and its potential as compared with current techniques (eg, based on physical dosimetry). And lastly, a section on optimization treatment planning efficiencies in which we explore the potential for machine learning in brachytherapy. As recent clinical evidence continues to show excellent outcomes, this is an exciting time to practice brachytherapy. With the new technologies presented here, the future is even brighter.

A GPU-based multi-criteria optimization algorithm for HDR brachytherapy.

Currently in HDR brachytherapy planning, a manual fine-tuning of an objective function is necessary to obtain case-specific valid plans. This study intends to facilitate this process by proposing a patient-specific inverse planning algorithm for HDR prostate brachytherapy: GPU-based multi-criteria optimization (gMCO). Two GPU-based optimization engines including simulated annealing (gSA) and a quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in parallel. After evaluating the equivalence and the computation performance of these two optimization engines, one preferred optimization engine was selected for the gMCO algorithm. Five hundred sixty-two previously treated prostate HDR cases were divided into validation set (100) and test set (462). In the validation set, the number of Pareto optimal plans to achieve the best plan quality was determined for the gMCO algorithm. In the test set, gMCO plans were compared with the physician-approved clinical plans. Our results indicated that the optimization process is equivalent between gL-BFGS and gSA, and that the computational performance of gL-BFGS is up to 67 times faster than gSA. Over 462 cases, the number of clinically valid plans was 428 (92.6%) for clinical plans and 461 (99.8%) for gMCO plans. The number of valid plans with target [Formula: see text] coverage greater than 95% was 288 (62.3%) for clinical plans and 414 (89.6%) for gMCO plans. The mean planning time was 9.4 s for the gMCO algorithm to generate 1000 Pareto optimal plans. In conclusion, gL-BFGS is able to compute thousands of SA equivalent treatment plans within a short time frame. Powered by gL-BFGS, an ultra-fast and robust multi-criteria optimization algorithm was implemented for HDR prostate brachytherapy. Plan pools with various trade-offs can be created with this algorithm. A large-scale comparison against physician approved clinical plans showed that treatment plan quality could be improved and planning time could be significantly reduced with the proposed gMCO algorithm.