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 ⩾ 98% and V ⩽ 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.
PurposeTo 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 software and deep learning.MethodsA 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.ResultsThe 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.ConclusionThe 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.
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 [Formula: see text] 98% and V [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 [Formula: see text] 98% and 2< V [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 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.
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 dayto-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 (V 95% ! 95% and V 107% 2%) for 37/46 plans using the 1-planlibrary 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 V 107% > 2% for 3/46 plans. Similar OAR results were obtained. Conclusion:The automated prior-plan adaptation method can successfully adapt for the large day-today variations observed in cervical cancer patients.
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 (V 95% ! 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 V 95% ! 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.
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