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Background: Deep learning has shown promising results to generate MRI‐based synthetic CTs and to enable accurate proton dose calculations on MRIs. For clinical implementation of synthetic CTs, quality assurance tools that verify their quality and reliability are required but still lacking. Purpose: This study aims to evaluate the predictive value of uncertainty maps generated with Monte Carlo dropout (MCD) for verifying proton dose calculations on deep‐learning‐based synthetic CTs (sCTs) derived from MRIs in online adaptive proton therapy. Methods: Two deep‐learning models (DCNN and cycleGAN) were trained for CT image synthesis using 101 paired CT‐MR images. sCT images were generated using MCD for each model by performing 10 inferences with activated dropout layers. The final sCT was obtained by averaging the inferred sCTs, while the uncertainty map was obtained from the HU variance corresponding to each voxel of 10 sCTs. The resulting uncertainty maps were compared to the observed HU‐, range‐, WET‐, and dose‐error maps between the sCT and planning CT. For range and WET errors, the generated uncertainty maps were projected along the 90‐degree angle. To evaluate the dose distribution, a mask based on the 5%‐isodose curve was applied to only include voxels along the beam paths. Pearson's correlation coefficients were calculated to determine the correlation between the uncertainty maps and HUs, range, WET, and dose errors. To evaluate the dosimetric accuracy of synthetic CTs, clinical proton treatment plans were recalculated and compared to the pCTs Results: Evaluation of the correlation showed an average of r = 0.92 ± 0.03 and r = 0.92 ± 0.03 for errors between uncertainty‐HU, r = 0.66 ± 0.09 and r = 0.62 ± 0.06 between uncertainty‐range, r = 0.64 ± 0.06 and r = 0.58 ± 0.07 between uncertainty‐WET, and r = 0.65 ± 0.09 and r = 0.67 ± 0.07 between uncertainty and dose difference for DCNN and cycleGAN model, respectively. Dosimetric comparison for target volumes showed an average 3%/3 mm gamma pass rate of 99.76 ± 0.43 (DCNN) and 99.10 ± 1.27 (cycleGAN). Conclusion: The observed correlations between uncertainty maps and the various metrics (HU, range, WET, and dose errors) demonstrated the potential of MCD‐based uncertainty maps as a reliable QA tool to evaluate the accuracy of deep learning‐based sCTs. [ABSTRACT FROM AUTHOR]

The aim of the study was to test the hypothesis that Intensity Modulated Proton Therapy (IMPT) reduces acute and late radiation toxicity in nasopharyngeal cancer (NPC) patients compared to photon-based radiation techniques including IMRT and VMAT. The study population of this prospective cohort study was composed of 131 NPC patients treated with curative radiotherapy (RT) or chemoradiotherapy. Between July 2007 and December 2017, all patients were treated with IMRT or VMAT. Since January 2018, 97 out of 99 patients (98%) qualified for IMPT according to model-based selection. All patients were included in a prospective data registration program in which acute and late toxicity was prospectively scored weekly during RT and at fixed time points after RT (6 weeks, 6, 12, 18 and 24 months). To determine the overall effect on acute and late toxicity, the Weighted Overall Toxicity Burden (WOTB) was calculated, defined as the sum of all toxicities weighted by toxicity grading. In addition, the WOTB Area Under the Curve (WOTB-AUC) was calculated representing the WOTB from the start of treatment until 24 months after completion of treatment. The two groups were well balanced regarding gender, age, race, T-stage, N-stage, AJCC-stage, and EBV-status. However, there was a significant difference regarding the chemotherapy regimens used between the two groups. In the photon cohort, 33% of patients were treated with conventional RT, 7% with concurrent chemoradiation, 2% with induction chemotherapy + concurrent chemoradiation and 57% with concurrent chemoradiation + adjuvant chemotherapy, while this was 25%, 37%, 36% and 2% in the proton cohort, respectively. The mean dose to all relevant organs at risk (i.e., oral cavity, pharyngeal constrictor muscles, parotid, and submandibular glands) were significantly lower with IMPT compared to photons. This was particularly true for the mean dose to the oral cavity which decreased from 27.2 Gy with IMRT/ VMAT to 10.7 Gy with IMPT (p<0.001). From January 2018, a plan comparison was made in all NPC patients referred for radiotherapy to our centre between IMPT and VMAT. In total, 97 out of 99 patients qualified for protons based on the estimated risk difference on dysphagia and xerostomia resulting from the dose reductions obtained with IMPT. IMPT resulted in significant reductions of various acute and late toxicities (Figure 1), including xerostomia, loss of taste, dysphagia, tube feeding dependence, sore mouth, and mucosal reactions. Only acute dermatitis was significantly worse at the end of IMPT, but completely recovered at 5 weeks after treatment in all patients. [Display omitted] The WOTB was significantly lower after IMPT (Figure 2) at all time points. In the IMPT group, the WOTB-AUC as measure for overall toxicity was 68% lower. The WOTB-AUC reduction was 60% in the acute phase (week 1 to 7), 69% in the recovery phase (from end of treatment to 6 months after RT) and 70% in the late phase (from 6 to 24 months after RT). In this prospective cohort study, patients treated with IMPT had statistically significant and clinically relevant reductions of various acute and late toxicities as compared to modern photon techniques like IMRT and VMAT as a historical control group. [ABSTRACT FROM AUTHOR]

There is a rising interest in developing and utilizing arc delivery techniques with charged particle beams, e.g., proton, carbon or other ions, for clinical implementation. In this work, perspectives from the European Society for Radiotherapy and Oncology (ESTRO) 2022 physics workshop on particle arc therapy are reported. This outlook provides an outline and prospective vision for the path forward to clinically deliverable proton, carbon, and other ion arc treatments. Through the collaboration among industry, academic, and clinical research and development, the scientific landscape and outlook for particle arc therapy are presented here to help our community understand the physics, radiobiology, and clinical principles. The work is presented in three main sections: (i) treatment planning, (ii) treatment delivery, and (iii) clinical outlook., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Sophie Wuyckens is funded by the Walloon Region as part of the Arc Proton Therapy convention (Pôles Mecatech et Biowin). Liu Hong and Guillaume Jansens are employed by ION BEAM APPLICATIONS SA (IBA Ltd). Research by Bas De Jong and Stefan Both is partly funded byION BEAM APPLICATIONS SA (IBA Ltd). Thomas Mackie is on the Board and has a financialinterest(stocks) and salary from Leo Cancer Care.Erik Engwall is employed by RaySearch. Martin Soukup is employed by Elekta. Xuanfeng Ding and Xiaoqiang Li have patents related to the particle arc therapy and it has been licensed to IBA. Xuanfeng Ding received honorium from IBA and Elekta’s speaker Bureau and the research project on proton arc therapy is in part supported by IBA and Elekta., (Copyright © 2024. Published by Elsevier B.V.)

Objective . Proton therapy currently faces challenges from clinical complications on organs-at-risk due to range uncertainties. To address this issue, positron emission tomography (PET) of the proton-induced 11 C and 15 O activity has been used to provide feedback on the proton range. However, this approach is not instantaneous due to the relatively long half-lives of these nuclides. An alternative nuclide, 12 N (half-life 11 ms), shows promise for real-time in vivo proton range verification. Development of 12 N imaging requires better knowledge of its production reaction cross section. Approach . The 12 C(p,n) 12 N reaction cross section was measured by detecting positron activity of graphite targets irradiated with 66.5, 120, and 150 MeV protons. A pulsed beam delivery with 0.7-2 × 10 8 protons per pulse was used. The positron activity was measured during the beam-off periods using a dual-head Siemens Biograph mCT PET scanner. The 12 N production was determined from activity time histograms. Main results . The cross section was calculated for 11 energies, ranging from 23.5 to 147 MeV, using information on the experimental setup and beam delivery. Through a comprehensive uncertainty propagation analysis, a statistical uncertainty of 2.6%-5.8% and a systematic uncertainty of 3.3%-4.6% were achieved. Additionally, a comparison between measured and simulated scanner sensitivity showed a scaling factor of 1.25 (±3%). Despite this, there was an improvement in the precision of the cross section measurement compared to values reported by the only previous study. Significance . Short-lived 12 N imaging is promising for real-time in vivo verification of the proton range to reduce clinical complications in proton therapy. The verification procedure requires experimental knowledge of the 12 N production cross section for proton energies of clinical importance, to be incorporated in a Monte Carlo framework for 12 N imaging prediction. This study is the first to achieve a precise measurement of the 12 C(p,n) 12 N nuclear cross section for such proton energies., (Creative Commons Attribution license.)