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AbstractBackgroundAccurate and efficient dose calculation is essential for on‐line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL‐based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities.PurposeTo develop a DL‐based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on‐line adaptive proton therapy clinical decision and subsequent replanning.MethodsPBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in‐house developed Monte‐Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs‐at‐risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D‐Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso‐dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency.ResultsCompared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s.ConclusionsAn accurate and efficient deep learning‐augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

AbstractBackgroundDeformable Image Registration (DIR) is an essential technique required in many applications of radiation oncology. However, conventional DIR approaches typically take several minutes to register one pair of 3D CT images and the resulting deformable vector fields (DVFs) are only specific to the pair of images used, making it less appealing for clinical application.PurposeA deep‐learning‐based DIR method using CT images is proposed for lung cancer patients to address the common drawbacks of the conventional DIR approaches and in turn can accelerate the speed of related applications, such as contour propagation, dose deformation, adaptive radiotherapy (ART), etc.MethodsA deep neural network based on VoxelMorph was developed to generate DVFs using CT images collected from 114 lung cancer patients. Two models were trained with the weighted mean absolute error (wMAE) loss and structural similarity index matrix (SSIM) loss (optional) (i.e., the MAE model and the M+S model). In total, 192 pairs of initial CT (iCT) and verification CT (vCT) were included as a training dataset and the other independent 10 pairs of CTs were included as a testing dataset. The vCTs usually were taken 2 weeks after the iCTs. The synthetic CTs (sCTs) were generated by warping the vCTs according to the DVFs generated by the pre‐trained model. The image quality of the synthetic CTs was evaluated by measuring the similarity between the iCTs and the sCTs generated by the proposed methods and the conventional DIR approaches, respectively. Per‐voxel absolute CT‐number‐difference volume histogram (CDVH) and MAE were used as the evaluation metrics. The time to generate the sCTs was also recorded and compared quantitatively. Contours were propagated using the derived DVFs and evaluated with SSIM. Forward dose calculations were done on the sCTs and the corresponding iCTs. Dose volume histograms (DVHs) were generated based on dose distributions on both iCTs and sCTs generated by two models, respectively. The clinically relevant DVH indices were derived for comparison. The resulted dose distributions were also compared using 3D Gamma analysis with thresholds of 3 mm/3%/10% and 2 mm/2%/10%, respectively.ResultsThe two models (wMAE and M+S) achieved a speed of 263.7±163 / 265.8±190 ms and a MAE of 13.15±3.8 / 17.52±5.8 HU for the testing dataset, respectively. The average SSIM scores of 0.987±0.006 and 0.988±0.004 were achieved by the two proposed models, respectively. For both models, CDVH of a typical patient showed that less than 5% of the voxels had a per‐voxel absolute CT‐number‐difference larger than 55 HU. The dose distribution calculated based on a typical sCT showed differences of ≤2cGy[RBE] for clinical target volume (CTV) D95 and D5, within ±0.06% for total lung V5, ≤1.5cGy[RBE] for heart and esophagus Dmean, and ≤6cGy[RBE] for cord Dmax compared to the dose distribution calculated based on the iCT. The good average 3D Gamma passing rates (> 96% for 3 mm/3%/10% and > 94% for 2 mm/2%/10%, respectively) were also observed.ConclusionA deep neural network‐based DIR approach was proposed and has been shown to be reasonably accurate and efficient to register the initial CTs and verification CTs in lung cancer.

2002 Background: Elderly patients with glioblastoma have a poor prognosis with phase III trials reporting median overall survival (OS) and global time to deterioration of 6–9 and 1.2 months respectively. 18F-DOPA (3,4-dihydroxy-6-[18F]-fluoro-L-phenylalanine) positron emission tomography (PET) is sensitive and specific for identifying regions of high density and biologically aggressive glioblastoma. Proton beam therapy (PBT) can spare more healthy tissue that may improve quality of life for this patient population. With improved targeting of disease along with dosimetric advantages of PBT, this phase II trial tested whether hypofractionated PBT can offer both improved survival and quality of life. Methods: Patients with newly diagnosed glioblastoma aged ≥65 years without contraindications to 18F-DOPA were eligible. Target volumes were defined by PET (tumor/normal brain SUV ≥2.0) and MRI areas of contrast enhancement including surgical cavity. Patients received dose escalated hypofractionated PBT of 35-40 GyE to PET/MRI over 5-10 treatments with 10 mm margin. Patients received concurrent/adjuvant temozolomide. With an alpha of 0.1, 18 of 39 patients would be required to be alive at 12 months for the study to be considered a success. Kaplan-Meier/Cox regression analysis was performed for progression free survival (PFS) and OS. Toxicities were evaluated with CTCAE v4.0. Patients completed EORTC BN20, QLQ-C30, and MMSE questionnaires. Time to deterioration, defined as a 10-point decrease in the score of the function domain or 10-point increase in score of symptom domain, was evaluated. Results: Between 5/2019-6/2021, 43 patients were enrolled, with 4 patients withdrawing due to progression/insurance denial prior to beginning protocol therapy. The median age was 70.2 yrs. and median follow-up was 12.5 months. MGMT methylation was present in 13 (33%). Tumors were multifocal in 10 (26%) and unifocal in 29 (74%). The primary endpoint was met with median PFS/OS was 6.5/12.9 months for all patients and for MGMT methylated patients 11.9/29.8 months respectively. Grade 3 baseline adjusted treatment related adverse events included 1 (3%) confusion, 2 (5%) fatigue, and 1 (3%) seizure. There were no grade 4/5 events. Multivariate analysis revealed MGMT unmethylated, ECOG 2 and biopsy only patients had worse survival. PET volume was more predictive than MRI volume for OS with PET volume>26 cc having a hazard ratio of 3.7. The median time to global deterioration per QLC-C30 was 6.3 months. Conclusions: This phase II trial incorporating 18F-DOPA PET-guided dose escalated hypofractionated proton beam treatment for elderly patients with glioblastoma met its primary endpoint for improved OS and patient reported quality of life compared to historical controls. Further investigation is warranted. Clinical trial information: NCT03778294 .

AbstractBackgroundMechanical accuracy should be verified before implementing a proton stereotactic radiosurgery (SRS) program. Linear accelerator (Linac)‐based SRS systems often use electronic portal imaging devices (EPIDs) to verify beam isocentricity. Because proton therapy systems do not have EPID, beam isocentricity tests of proton SRS may still rely on films, which are not efficient.PurposeTo validate that our proton SRS system meets mechanical precision requirements and to present an efficient method to evaluate the couch and gantry's rotational isocentricity for our proton SRS system.MethodsA dedicated applicator to hold brass aperture for proton SRS system was designed. The mechanical precision of the system was tested using a metal ball and film for 11 combinations of gantry and couch angles. A more efficient quality assurance (QA) procedure was developed, which used a scintillator device to replace the film. The couch rotational isocentricity tests were performed using orthogonal kV x‐rays with the couch rotated isocentrically to five positions (0°, 315°, 270°, 225°, and 180°). At each couch position, the distance between the metal ball in kV images and the imaging isocenter was measured. The gantry isocentricity tests were performed using a cone‐shaped scintillator and proton beams at five gantry angles (0°, 45°, 90°, 135°, and 180°), and the isocenter position and the distance of each beam path to the isocenter were obtained. Daily QA procedure was performed for 1 month to test the robustness and reproducibility of the procedure.ResultsThe gantry and couch rotational isocentricity exhibited sub‐mm precision, with most measurements within ±0.5 mm. The 1‐month QA results showed that the procedure was robust and highly reproducible to within ±0.2 mm. The gantry isocentricity test using the cone‐shaped scintillator was accurate and sensitive to variations of ±0.2 mm. The QA procedure was efficient enough to be completed within 30 min. The 1‐month isocentricity position variations were within 0.5 mm, which demonstrating that the overall proton SRS system was stable and precise.ConclusionThe proton SRS Winston–Lutz QA procedure using a cone‐shaped scintillator was efficient and robust. We were able to verify radiation delivery could be performed with sub‐mm mechanical precision.

AbstractBackgroundParallel‐opposed lateral beams are the conventional beam arrangements in proton therapy for prostate cancer. However, when considering linear energy transfer (LET) and RBE effects, alternative beam arrangements should be investigated.PurposeTo investigate the dose and dose averaged LET (LETd) impact of using new beam arrangements rotating beams 5°–15° posteriorly to the laterals in prostate cancer treated with pencil‐beam‐scanning (PBS) proton therapy.MethodsTwenty patients with localized prostate cancer were included in this study. Four proton treatment plans for each patient were generated utilizing 0°, 5°, 10°, and 15° posterior oblique beam pairs relative to parallel‐opposed lateral beams. Dose‐volume histograms (DVHs) from posterior oblique beams were analyzed. Dose‐LETd‐volume histogram (DLVH) was employed to study the difference in dose and LETd with each beam arrangement. DLVH indices, , defined as the cumulative absolute volume that has a dose of at least d (Gy[RBE]) and a LETd of at least l (keV/µm), were calculated for both the rectum and bladder to the whole group of patients and two‐sub groups with and without hydrogel spacer. These metrics were tested using Wilcoxon signed‐rank test.ResultsRotating beam angles from laterals to slightly posterior by 5°–15° reduced high LETd volumes while it increased the dose volume in the rectum and increased LETd in bladders. Beam angles rotated five degrees posteriorly from laterals (i.e., gantry in 95° and 265°) are proposed since they achieved the optimal balance of better LETd sparing and minimal dose increase in the rectum. A reduction of V(50 Gy[RBE], 2.6 keV/µm) from 7.41 to 3.96 cc (p < 0.01), and a slight increase of V(50 Gy[RBE], 0 keV/µm) from 20.1 to 21.6 cc (p < 0.01) were observed for the group without hydrogel spacer. The LETd sparing was less effective for the group with hydrogel spacer, which achieved the reduction of V(50 Gy[RBE], 2.6 keV/µm) from 4.28 to 2.10 cc (p < 0.01).ConclusionsPosterior oblique angle plans improved LETd sparing of the rectum while sacrificing LETd sparing in the bladder in the treatment of prostate cancer with PBS. Beam angle modification from laterals to slightly posterior may be a strategy to redistribute LETd and perhaps reduce rectal toxicity risks in prostate cancer patients treated with PBS. However, the effect is reduced for patients with hydrogel spacer.

AbstractBackgroundIn proton therapy dose calculation, Monte Carlo (MC) simulations are superior in accuracy but more time consuming, compared to analytical calculations. Graphic processing units (GPUs) are effective in accelerating MC simulations but may suffer thread divergence and racing condition in GPU threads that degrades the computing performance due to the generation of secondary particles during nuclear reactions.PurposeA novel concept of virtual particle (VP) MC (VPMC) is proposed to avoid simulating secondary particles in GPU‐accelerated proton MC dose calculation and take full advantage of the computing power of GPU.MethodsNeutrons and gamma rays were ignored as escaping from the human body; doses of electrons, heavy ions, and nuclear fragments were locally deposited; the tracks of deuterons were converted into tracks of protons. These particles, together with primary and secondary protons, are considered to be the realistic particles. Histories of primary and secondary protons were replaced by histories of multiple VPs. Each VP corresponded to one proton (either primary or secondary). A continuous‐slowing‐down‐approximation model, an ionization model, and a large angle scattering event model corresponding to nuclear interactions were developed for VPs by generating probability distribution functions (PDFs) based on simulation results of realistic particles using MCsquare. For efficient calculations, these PDFs were stored in the Compute Unified Device Architecture textures. VPMC was benchmarked with TOPAS and MCsquare in phantoms and with MCsquare in 13 representative patient geometries. Comparisons between the VPMC calculated dose and dose measured in water during patient‐specific quality assurance (PSQA) of the selected 13 patients were also carried out. Gamma analysis was used to compare the doses derived from different methods and calculation efficiencies were also compared.ResultsIntegrated depth dose and lateral dose profiles in both homogeneous and inhomogeneous phantoms all matched well among VPMC, TOPAS, and MCsquare calculations. The 3D–3D gamma passing rates with a criterion of 2%/2 mm and a threshold of 10% was 98.49% between MCsquare and TOPAS and 98.31% between VPMC and TOPAS in homogeneous phantoms, and 99.18% between MCsquare and TOPAS and 98.49% between VPMC and TOPAS in inhomogeneous phantoms, respectively. In patient geometries, the 3D–3D gamma passing rates with 2%/2 mm/10% between dose distributions from VPMC and MCsquare were 98.56 ± 1.09% in patient geometries. The 2D–3D gamma analysis with 3%/2 mm/10% between the VPMC calculated dose distributions and the 2D measured planar dose distributions during PSQA was 98.91 ± 0.88%. VPMC calculation was highly efficient and took 2.84 ± 2.44 s to finish for the selected 13 patients running on four NVIDIA Ampere GPUs in patient geometries.ConclusionVPMC was found to achieve high accuracy and efficiency in proton therapy dose calculation.