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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]

Simple Summary: While the dosimetric advantages of proton therapy over conventional treatment for CSI are typically emphasized for spine fields, only lens dose reduction has been reported for brain fields. In this retrospective in silico planning study, the aim is to assess the potential gains of proton CSI for organs at risk in the whole-brain part of CSI. Different optimization techniques and beam arrangements as well as pencil beam spot editing were employed to determine the potential gains. The study shows that: (1) cochlear sparing is impractical, (2) improving the lens dose is possible mainly by adding a posterior field direction, and (3) an incremental scalp dose reduction is possible, particularly in adult patients. Proton therapy is a promising modality for craniospinal irradiation (CSI), offering dosimetric advantages over conventional treatments. While significant attention has been paid to spine fields, for the brain fields, only dose reduction to the lens of the eye has been reported. Hence, the objective of this study is to assess the potential gains and feasibility of adopting different treatment planning techniques for the entire brain within the CSI target. To this end, eight previously treated CSI patients underwent retrospective replanning using various techniques: (1) intensity modulated proton therapy (IMPT) optimization, (2) the modification/addition of field directions, and (3) the pre-optimization removal of superficially placed spots. The target coverage robustness was evaluated and dose comparisons for lenses, cochleae, and scalp were conducted, considering potential biological dose increases. The target coverage robustness was maintained across all plans, with minor reductions when superficial spot removal was utilized. Single- and multifield optimization showed comparable target coverage robustness and organ-at-risk sparing. A significant scalp sparing was achieved in adults but only limited in pediatric cases. Superficial spot removal contributed to scalp V30 Gy reduction at the expense of lower coverage robustness in specific cases. Lens sparing benefits from multiple field directions, while cochlear sparing remains impractical. Based on the results, all investigated plan types are deemed clinically adoptable. [ABSTRACT FROM AUTHOR]

Background: Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning‐based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT‐based proton dose calculations. Purpose: To compare daily CBCT‐based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT‐based sCTs in a scenario mimicking actual clinical practice. Methods: Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs/pCT was quantified by calculating the D98 difference for target volumes and Dmean difference for organs‐at‐risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia. Results: For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within ±1.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within ±2.5% for 90% of the sCTs. Conclusions: Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Personalized treatment is increasingly required for oropharyngeal squamous cell carcinoma (OPSCC) patients due to emerging new cancer subtypes and treatment options. Outcome prediction model can help identify low or high‐risk patients who may be suitable to receive de‐escalation or intensified treatment approaches. Purpose: To develop a deep learning (DL)‐based model for predicting multiple and associated efficacy endpoints in OPSCC patients based on computed tomography (CT). Methods: Two patient cohorts were used in this study: a development cohort consisting of 524 OPSCC patients (70% for training and 30% for independent testing) and an external test cohort of 396 patients. Pre‐treatment CT‐scans with the gross primary tumor volume contours (GTVt) and clinical parameters were available to predict endpoints, including 2‐year local control (LC), regional control (RC), locoregional control (LRC), distant metastasis‐free survival (DMFS), disease‐specific survival (DSS), overall survival (OS), and disease‐free survival (DFS). We proposed DL outcome prediction models with the multi‐label learning (MLL) strategy that integrates the associations of different endpoints based on clinical factors and CT‐scans. Results: The multi‐label learning models outperformed the models that were developed based on a single endpoint for all endpoints especially with high AUCs ≥ 0.80 for 2‐year RC, DMFS, DSS, OS, and DFS in the internal independent test set and for all endpoints except 2‐year LRC in the external test set. Furthermore, with the models developed, patients could be stratified into high and low‐risk groups that were significantly different for all endpoints in the internal test set and for all endpoints except DMFS in the external test set. Conclusion: MLL models demonstrated better discriminative ability for all 2‐year efficacy endpoints than single outcome models in the internal test and for all endpoints except LRC in the external set. [ABSTRACT FROM AUTHOR]

Background: Proton arcs have shown potential to reduce the dose to organs at risks (OARs) by delivering the protons from many different directions. While most previous studies have been focused on dynamic arcs (delivery during rotation), an alternative approach is discrete arcs, where step‐and‐shoot delivery is used over a large number of beam directions. The major advantage of discrete arcs is that they can be delivered at existing proton facilities. However, this advantage comes at the expense of longer treatment times. Purpose: To exploit the dosimetric advantages of proton arcs, while achieving reasonable delivery times, we propose a partitioning approach where discrete arc plans are split into subplans to be delivered over different fractions in the treatment course. Methods: For three oropharyngeal cancer patients, four different arc plans have been created and compared to the corresponding clinical IMPT plan. The treatment plans are all planned to be delivered in 35 fractions, but with different delivery approaches over the fractions. The first arc plan (1×30) has 30 directions to be delivered every fraction, while the others are partitioned into subplans with 10 and 6 beam directions, each to be delivered every third (3×10), fifth fraction (5×6), or seventh fraction (7×10). All plans are assessed with respect to delivery time, target robustness over the treatment course, doses to OARs and NTCP for dysphagia and xerostomia. Results: The delivery time (including an additional delay of 30 s between the discrete directions to simulate manual interaction with the treatment control system) is reduced from on average 25.2 min for the 1×30 plan to 9.2 min for the 3×10 and 7×10 plans and 5.7 min for the 5×6 plans. The delivery time for the IMPT plan is 7.9 min. When accounting for the combination of delivery time, target robustness, OAR sparing, and NTCP reduction, the plans with 10 directions in each fraction are the preferred choice. Both the 3×10 and 7×10 plans show improved target robustness compared to the 1×30 plans, while keeping OAR doses and NTCP values at almost as low levels as for the 1×30 plans. For all patients the NTCP values for dysphagia are lower for the partitioned plans with 10 directions compared to the IMPT plans. NTCP reduction for xerostomia compared to IMPT is seen in two of the three patients. The best results are seen for the first patient, where the NTCP reductions for the 7×10 plan are 1.6 p.p. (grade 2 xerostomia) and 1.5 p.p. (grade 2 dysphagia). The corresponding NTCP reductions for the 1×30 plan are 2.7 p.p. (xerostomia, grade 2) and 2.0 p.p. (dysphagia, grade 2). Conclusions: Discrete proton arcs can be implemented at any proton facility with reasonable treatment times using a partitioning approach. The technique also makes the proton arc treatments more robust to changes in the patient anatomy. [ABSTRACT FROM AUTHOR]

Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approaches openly. This paper describes a dataset of brain and pelvis computed tomography (CT) images with rigidly registered cone‐beam CT (CBCT) and magnetic resonance imaging (MRI) images to facilitate the development and evaluation of sCT generation for radiotherapy planning. Acquisition and Validation Methods: The dataset consists of CT, CBCT, and MRI of 540 brains and 540 pelvic radiotherapy patients from three Dutch university medical centers. Subjects' ages ranged from 3 to 93 years, with a mean age of 60. Various scanner models and acquisition settings were used across patients from the three data‐providing centers. Details are available in a comma separated value files provided with the datasets. Data Format and Usage Notes: The data is available on Zenodo (https://doi.org/10.5281/zenodo.7260704, https://doi.org/10.5281/zenodo.7868168) under the SynthRAD2023 collection. The images for each subject are available in nifti format. Potential Applications: This dataset will enable the evaluation and development of image synthesis algorithms for radiotherapy purposes on a realistic multi‐center dataset with varying acquisition protocols. Synthetic CT generation has numerous applications in radiation therapy, including diagnosis, treatment planning, treatment monitoring, and surgical planning. [ABSTRACT FROM AUTHOR]

Background: Proton radiography (PR) uses highly energetic proton beams to create images where energy loss is the main contrast mechanism. Waterequivalent path length (WEPL) measurements using flat panel PR (FP-PR) have potential for in vivo range verification. However, an accurate WEPL measurement via FP-PR requires irradiation with multiple energy layers, imposing high imaging doses. Purpose: A FP-PR method is proposed for accurate WEPL determination based on a patient-specific imaging field with a reduced number of energies (n) to minimize imaging dose. Methods: Patient-specific FP-PRs were simulated and measured for a head and neck (HN) phantom.An energy selection algorithm estimated spot-wise the lowest energy required to cross the anatomy (Emin) using a water-equivalent thickness map.Starting from Emin, n was restricted to certain values (n = 26, 24, 22, …, 2 for simulations, n = 10 for measurements), resulting in patient-specific FP-PRs. A reference FP-PR with a complete set of energies was compared against patient-specific FP-PRs covering the whole anatomy via mean absolute WEPL differences (MAD), to evaluate the impact of the developed algorithm. WEPL accuracy of patient-specific FP-PRs was assessed using mean relative WEPL errors (MRE) with respect to measured multi-layer ionization chamber PRs (MLIC-PR) in the base of skull, brain, and neck regions. Results: MADs ranged from 2.1 mm (n = 26) to 21.0 mm (n = 2) for simulated FP-PRs, and 7.2 mm for measured FP-PRs (n = 10). WEPL differences below 1 mm were observed across the whole anatomy,except at the phantom surfaces. Measured patient-specific FP-PRs showed good agreement against MLIC-PRs, with MREs of 1.3 ± 2.0%, −0.1 ± 1.0%, and −0.1 ± 0.4% in the three regions of the phantom. Conclusion: A method to obtain accurate WEPL measurements using FP-PR with a reduced number of energies selected for the individual patient anatomy was established in silico and validated experimentally. Patient-specific FP-PRs could provide means of in vivo range verification. [ABSTRACT FROM AUTHOR]

Background: Proton arc technology has recently shown dosimetric gains for various treatment indications. The increased number of beams and energy layers (ELs) in proton arc plans, increases the degrees of freedom in plan optimization and thereby flexibility to spare dose in organs at risk (OARs).A relationship exists between dosimetric plan quality, delivery efficiency, the number of ELs -and beams in a proton arc plan. Purpose: This work aims to investigate the effect of the number of beams and ELs in a proton arc plan, on toxicity and delivery time for oropharyngeal cancer patients (OPC) selected for intensity modulated proton therapy (IMPT) based on the Dutch model-based approach. Methods: The EL reduction algorithm iteratively selects ELs from beams equidistantly spaced over a 360◦ arc. The beams in the final plan may contain multiple ELs, making them suited for static delivery on the studied treatment machine. The produced plans can therefore be called “step and shoot” proton arc plans. The number of beams and ELs were varied to determine the relationship with the planning cost function value, normal tissue complication probability (NTCP) and delivery time. Proton arc plans with robust target coverage and optimal energy layer reduction (ELR) settings to reduce NTCP, were generated for 10 OPC patients. Proton arc plans were compared to clinical volumetric modulated arc therapy (VMAT) and IMPT plans in terms of integral dose, OAR dose,NTCP for xerostomia and dysphagia and delivery time. Furthermore, dose-weighted average linear energy transfer (LETd) distributions were compared between the IMPT and proton arc plans. A dry run delivery of a plan containing 20 beams and 360 ELs was performed to evaluate delivery time and accuracy. Results: We found 360 ELs distributed over 30 beams generated proton arc plans with near minimal expected plan toxicity. Relative to corresponding IMPT and VMAT plans, an average reduction of 21 ± 3% and 58 ± 10% in integral dose was observed. Dmean was reduced most in the pharyngeal constrictor muscle (PCM) medius structure, with on average 9.0 ± 4.2 Gy(RBE) (p = 0.0002) compared to the clinical IMPT plans. The average NTCP for grade≥2 and grade≥3 xerostomia at 6 months after treatment significantly decreased with 4.7 ± 1.8% (p = 0.002) and 1.7 ± 0.8% (p = 0.002), respectively, while the average NTCP for grade≥2 and grade≥3 dysphagia decreased with 4.4 ± 2.9% (p = 0.002) and 0.9 ± 0.4% (p = 0.002), respectively, increasing the benefit of protons relative to VMAT. For a “step and shoot” proton arc delivery with auto beam sequencing the estimated delivery time is 11 min, similar to the delivery time of a 6-field IMPT treatment. Gamma analysis between the planned and delivered dose distribution resulted in a 99.99% pass rate using 1mm/1% dose difference/distance to agreement criteria. Conclusions: “Step and shoot” proton arc demonstrates potential to further reduce toxicity compared to IMPT and VMAT in OPC treatment. By employing 360 ELs and 30 beams in the proposed ELR method, delivery time can reach clinically acceptable levels without compromising plan toxicity when automatic beam sequencing is available. [ABSTRACT FROM AUTHOR]