Benchmarking daily adaptation using fully automated radiotherapy treatment plan optimization for rectal cancer.

Background/purpose: In daily plan adaptation the radiotherapy treatment plan is adjusted just prior to delivery. A simple approach is taking the planning objectives of the reference plan and directly applying these in re-optimization. Here we present a tested method to verify whether daily adaptation without tweaking of the objectives can maintain the plan quality throughout treatment. Materials/methods: For fifteen rectal cancer patients, automated treatment planning was used to generate plans mimicking manual reference plans on the planning scans. For 74 fraction scans (4-5 per patient) an automated plan and a daily adapted plan were generated, where the latter re-optimizes the reference plan objectives without any tweaking. To evaluate the robustness of the daily adaptation, the adapted plans were compared to the autoplanning plans. Results: Median differences between the autoplanning plans on the planning scans and the reference plans were between -1 and 0.2 Gy. The largest interquartile range (1 Gy) was seen for the Lumbar Skin D2%. For the daily scans the PTV D2% and D98% differences between autoplanning and adapted plans were within ± 0.7 Gy, with mean differences within ± 0.3 Gy. Positive differences indicate higher values were obtained using autoplanning. For the Bowelarea + Bladder and the Lumbar Skin the D2% and Dmean differences were all within ± 2.6 Gy, with mean differences between -0.9 and 0.1 Gy. Conclusion: Automated treatment planning can be used to benchmark daily adaptation techniques. The investigated adaptation workflow can robustly perform high quality adaptations without daily adjusting of the patient-specific planning objectives for rectal cancer radiotherapy.

Evaluation of alternative parameter settings for dose restoration and full plan adaptation in IMPT for prostate cancer.

BACKGROUND/PURPOSE: Intensity-modulated proton therapy is highly sensitive to anatomical variations. A dose restoration method and a full plan adaptation method have been developed earlier, both requiring several parameter settings. This study evaluates the validity of the previously selected settings by systematically comparing them to alternatives. MATERIALS/METHODS: The dose restoration method takes a prior plan and uses an energy-adaptation followed by a spot-intensity re-optimization to restore the plan to its initial state. The full adaptation method uses an energy-adaptation followed by the addition of new spots and a spot-intensity optimization to fit the new anatomy. We varied: 1) The margins and robustness settings of the prior plan, 2) the spot-addition sample size, i.e. the number of added spots, 3) the spot-addition stopping criterion, and 4) the spot-intensity optimization approach. The last three were evaluated only for the full plan adaptation. Evaluations were done on 88 CT scans of 11 prostate cancer patients. Dose was prescribed as 55 Gy(RBE) to the lymph nodes and seminal vesicles with a boost to 74 Gy(RBE) to the prostate. RESULTS: For the dose restoration method, changing the applied CTV-to-PTV margins and plan robustness in the prior plans yielded insufficient target coverage or increased OAR doses. For the full plan adaptation, more spot-addition iterations and using a different optimization approach resulted in lower OAR doses compared to the default settings while maintaining target coverage. However, the calculation times increased by up to 20 times, making these variations infeasible for online-adaptation. CONCLUSION: We recommend maintaining the default setting for the dose restoration approach. For the full plan adaptation we recommend to focus on fine-tuning the optimization-parameters, and apart from this using the default settings.

Dosimetric Impact of Intrafraction Motion in Online-Adaptive Intensity Modulated Proton Therapy for Cervical Cancer.

PURPOSE: A method was recently developed for online-adaptive intensity modulated proton therapy (IMPT) in patients with cervical cancer. The advantage of this approach, relying on the use of tight margins, is challenged by the intrafraction target motion. The purpose of this study was to evaluate the dosimetric effect of intrafraction motion on the target owing to changes in bladder filling in patients with cervical cancer treated with online-adaptive IMPT. METHODS AND MATERIALS: In 10 patients selected to have large uterus motion induced by bladder filling, the intrafraction anatomic changes were simulated for several prefraction durations for online (automated) contouring and planning. For each scenario, the coverage of the primary target was evaluated with margins of 2.5 and 5 mm. RESULTS: Using a 5- mm planning target volume margin, median accumulated D98% was greater than 42.75 GyRBE1.1 (95% of the prescribed dose) in the case of a prefraction duration of 5 and 10 minutes. For a prefraction duration of 15 minutes, this parameter deteriorated to 42.6 GyRBE1.1. When margins were reduced to 2.5 mm, only a 5-minute duration resulted in median target D98% above 42.75 GyRBE1.1. In addition, smaller bladders were found to be associated with larger dose degradations compared with larger bladders. CONCLUSIONS: This study indicates that intrafraction anatomic changes can have a substantial dosimetric effect on target coverage in an online-adaptive IMPT scenario for patients subject to large uterus motion. A margin of 5 mm was sufficient to compensate for the intrafraction motion due to bladder filling for up to 10 minutes of prefraction time. However, compensation for the uncertainties that were disregarded in this study, by using margins or robust optimization, is also required. Furthermore, a large bladder volume restrains intrafraction target motion and is recommended for treating patients in this scenario. Assuming that online-adaptive IMPT remains beneficial as long as narrow margins are used (5 mm or below), this study demonstrates its feasibility with regard to intrafraction motion.

Online-adaptive versus robust IMPT for prostate cancer: How much can we gain?

BACKGROUND/PURPOSE: Intensity-modulated proton therapy (IMPT) is highly sensitive to anatomical variations which can cause inadequate target coverage during treatment. Available mitigation techniques include robust treatment planning and online-adaptive IMPT. This study compares a robust planning strategy to two online-adaptive IMPT strategies to determine the benefit of online adaptation. MATERIALS/METHODS: We derived the robustness settings and safety margins needed to yield adequate target coverage (V95%≥98%) for >90% of 11 patients in a prostate cancer cohort (88 repeat CTs). For each patient, we also adapted a non-robust prior plan using a simple restoration and a full adaptation method. The restoration uses energy-adaptation followed by a fast spot-intensity re-optimization. The full adaptation uses energy-adaptation followed by the addition of new spots and a range-robust spot-intensity optimization. Dose was prescribed as 55 Gy(RBE) to the low-dose target (lymph nodes and seminal vesicles) with a boost to 74 Gy(RBE) to the high-dose target (prostate). Daily patient set-up was simulated using implanted intra-prostatic markers. RESULTS: Margins of 4 and 8 mm around the high- and low-dose target regions, a 6 mm setup error and a 3% range error were found to obtain adequate target coverage for all repeat CTs of 10/11 patients (94.3% of all 88 repeat CTs). Both online-adaptive strategies yielded V95%≥98% and better OAR sparing in 11/11 patients. Median OAR improvements up to 11%-point and 16%-point were observed when moving from robust planning to respectively restoration and full adaption. CONCLUSION: Both full plan adaptation and simple dose restoration can increase OAR sparing besides better conforming to the target criteria compared to robust treatment planning.

Evaluation of an Open Source Registration Package for Automatic Contour Propagation in Online Adaptive Intensity-Modulated Proton Therapy of Prostate Cancer.

Objective: Our goal was to investigate the performance of an open source deformable image registration package, elastix, for fast and robust contour propagation in the context of online-adaptive intensity-modulated proton therapy (IMPT) for prostate cancer. Methods: A planning and 7-10 repeat CT scans were available of 18 prostate cancer patients. Automatic contour propagation of repeat CT scans was performed using elastix and compared with manual delineations in terms of geometric accuracy and runtime. Dosimetric accuracy was quantified by generating IMPT plans using the propagated contours expanded with a 2 mm (prostate) and 3.5 mm margin (seminal vesicles and lymph nodes) and calculating dosimetric coverage based on the manual delineation. A coverage of V 95% ≥ 98% (at least 98% of the target volumes receive at least 95% of the prescribed dose) was considered clinically acceptable. Results: Contour propagation runtime varied between 3 and 30 s for different registration settings. For the fastest setting, 83 in 93 (89.2%), 73 in 93 (78.5%), and 91 in 93 (97.9%) registrations yielded clinically acceptable dosimetric coverage of the prostate, seminal vesicles, and lymph nodes, respectively. For the prostate, seminal vesicles, and lymph nodes the Dice Similarity Coefficient (DSC) was 0.87 ± 0.05, 0.63 ± 0.18, and 0.89 ± 0.03 and the mean surface distance (MSD) was 1.4 ± 0.5 mm, 2.0 ± 1.2 mm, and 1.5 ± 0.4 mm, respectively. Conclusion: With a dosimetric success rate of 78.5-97.9%, this software may facilitate online adaptive IMPT of prostate cancer using a fast, free and open implementation.

Plan-library supported automated replanning for online-adaptive intensity-modulated proton therapy of cervical cancer.

Background: Intensity-modulated proton therapy is sensitive to inter-fraction variations, including density changes along the pencil-beam paths and variations in organ-shape and location. Large day-to-day variations are seen for cervical cancer patients. The purpose of this study was to develop and evaluate a novel method for online selection of a plan from a patient-specific library of prior plans for different anatomies, and adapt it for the daily anatomy. Material and methods: The patient-specific library of prior plans accounting for altered target geometries was generated using a pretreatment established target motion model. Each fraction, the best fitting prior plan was selected. This prior plan was adapted using (1) a restoration of spot-positions (Bragg peaks) by adapting the energies to the new water equivalent path lengths; and (2) a spot addition to fully cover the target of the day, followed by a fast optimization of the spot-weights with the reference point method (RPM) to obtain a Pareto-optimal plan for the daily anatomy. Spot addition and spot-weight optimization could be repeated iteratively. The patient cohort consisted of six patients with in total 23 repeat-CT scans, with a prescribed dose of 45 Gy(RBE) to the primary tumor and the nodal CTV. Using a 1-plan-library (one prior plan based on all motion in the motion model) was compared to choosing from a 2-plan-library (two prior plans based on part of the motion). Results: Applying the prior-plan adaptation method with one iteration of adding spots resulted in clinically acceptable target coverage ( V95%≥95% and V107%≤2% ) for 37/46 plans using the 1-plan-library and 41/46 plans for the 2-plan-library. When adding spots twice, the 2-plan-library approach could obtain acceptable coverage for all scans, while the 1-plan-library approach showed V107%>2% for 3/46 plans. Similar OAR results were obtained. Conclusion: The automated prior-plan adaptation method can successfully adapt for the large day-to-day variations observed in cervical cancer patients.

Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer.

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.

Correlations between the shifts in prompt gamma emission profiles and the changes in daily target coverage during simulated pencil beam scanning proton therapy.

The aim of this study was to investigate the feasibility of using prompt gamma (PG) ray emission profiles to monitor changes in dose to the planning target volume (PTV) during pencil beam scanning (PBS) proton therapy as a result of day-to-day variation in patient anatomy. For 11 prostate patients, we simulated treatment plan delivery using the patients' daily anatomy as observed in the planning CT and 7-9 control CT scans, including the detected PG profiles resulting from the 5%, 10%, and 20% most intense proton pencil beams. For each patient, we determined the changes in dosimetric parameters for the high- and low-dose PTVs between the simulations performed using the planning CT scan and the different control CT scans and correlated these to changes in the PG emission profiles. Changes in coverage of the high- and low-dose PTV correlated most strongly with the median and mean absolute PG emission profile shifts of the 5% most intense pencil beams, respectively. With a mean Pearson correlation coefficient of -0.76 (SD: 0.17) for the high-dose PTV and of -0.60 (SD: 0.51) for the low-dose PTV. We showed, as a proof of principle, that PG emission profiles obtained during PBS proton therapy could be used to detect changes in PTV coverage due to day-to-day anatomical variation.

An automated planning strategy for near real-time adaptive proton therapy in prostate cancer.

Proton therapy plans are very sensitive to anatomical changes such as density changes along the pencil-beam paths and changes in organ shape and location. Previously, we developed a restoration method which compensates for density changes along the pencil-beam paths but which is unable to adapt for anatomical changes. This study's purpose is to develop and evaluate an automated method for adaptation of IMPT plans in near real-time to the anatomy of the day. We developed an automated treatment plan adaptation method using (1) a restoration of spot positions (Bragg peaks) by adapting the energies to the new water equivalent path lengths; and (2) a spot addition to fully cover the target of the day, followed by a fast reference point method optimization of the spot weights resulting in a Pareto optimal plan for the daily anatomy. The method was developed and evaluated using 8-10 repeat CT scans of 11 prostate cancer patients, prescribing 55 Gy(RBE) (seminal vesicles and lymph nodes) with a boost to 74 Gy(RBE) (prostate). Applying the automated adaptation method resulted in a clinically acceptable target coverage (V 95% [Formula: see text] 98% and V 107% [Formula: see text] 2%) for 96% of the scans after a single iteration of adding 2500 spots. The other scans obtained target coverages with V 95% [Formula: see text] 98% and 2 < V 107% [Formula: see text] 5%. When using two spot-addition iterations, all scans obtained clinically acceptable results. Compared to the restoration method the adaptation lowered the mean dose to rectum and bladder with median values of 6.2 Gy(RBE) and 4.7 Gy(RBE) respectively. The largest improvements were obtained for V 45Gy(RBE) for both rectum and bladder, with median differences of 10.3%-point and 10.8%-point respectively, and maximum differences up to 22%-point. The two adaptation steps took on average 7.3 s and 1.7 min respectively. No user interaction was needed, making this fast and fully automated method a first step towards online adaptive proton therapy.

Near real-time automated dose restoration in IMPT to compensate for daily tissue density variations in prostate cancer.

Proton therapy is very sensitive to daily density changes along the pencil beam paths. The purpose of this study is to develop and evaluate an automated method for adaptation of IMPT plans to compensate for these daily tissue density variations. A two-step restoration method for 'densities-of-the-day' was created: (1) restoration of spot positions (Bragg peaks) by adapting the energy of each pencil beam to the new water equivalent path length; and (2) re-optimization of pencil beam weights by minimizing the dosimetric difference with the planned dose distribution, using a fast and exact quadratic solver. The method was developed and evaluated using 8-10 repeat CT scans of 10 prostate cancer patients. Experiments demonstrated that giving a high weight to the PTV in the re-optimization resulted in clinically acceptable restorations. For all scans we obtained V 95% ⩾ 98% and V 107% ⩽ 2%. For the bladder, the differences between the restored and the intended treatment plan were below +2 Gy and +2%-point. The rectum differences were below +2 Gy and +2%-point for 90% of the scans. In the remaining scans the rectum was filled with air, which partly overlapped with the PTV. The air cavity distorted the Bragg peak resulting in less favorable rectum doses.