Technical note: Dosimetry and FLASH potential of UHDR proton PBS for small lung tumors: Bragg-peak-based delivery versus transmission beam and IMPT.

BACKGROUND: High-energy transmission beams (TBs) are currently the main delivery method for proton pencil beam scanning ultrahigh dose-rate (UHDR) FLASH radiotherapy. TBs place the Bragg-peaks behind the target, outside the patient, making delivery practical and achievement of high dose-rates more likely. However, they lead to higher integral dose compared to conventional intensity-modulated proton therapy (IMPT), in which Bragg-peaks are placed within the tumor. It is hypothesized that, when energy changes are not required and high beam currents are possible, Bragg-peak-based beams can not only achieve more conformal dose distributions than TBs, but also have more FLASH-potential. PURPOSE: This works aims to verify this hypothesis by taking three different Bragg-peak-based delivery techniques and comparing them with TB and IMPT-plans in terms of dosimetry and FLASH-potential for single-fraction lung stereotactic body radiotherapy (SBRT). METHODS: For a peripherally located lung target of various sizes, five different proton plans were made using "matRad" and inhouse-developed algorithms for spot/energy-layer/beam reduction and minimum monitor unit maximization: (1) IMPT-plan, reference for dosimetry, (2) TB-plan, reference for FLASH-amount, (3) pristine Bragg-peak plan (non-depth-modulated Bragg-peaks), (4) Bragg-peak plan using generic ridge filter, and (5) Bragg-peak plan using 3D range-modulated ridge filter. RESULTS: Bragg-peak-based plans are able to achieve sufficient plan quality and high dose-rates. IMPT-plans resulted in lowest OAR-dose and integral dose (also after a FLASH sparing-effect of 30%) compared to both TB-plans and Bragg-peak-based plans. Bragg-peak-based plans vary only slightly between themselves and generally achieve lower integral dose than TB-plans. However, TB-plans nearly always resulted in lower mean lung dose than Bragg-peak-based plans and due to a higher amount of FLASH-dose for TB-plans, this difference increased after including a FLASH sparing-effect. CONCLUSION: This work indicates that there is no benefit in using Bragg-peak-based beams instead of TBs for peripherally located, UHDR stereotactic lung radiotherapy, if lung dose is the priority.

Spine Stereotactic Body Radiation Therapy Without Immobilization: Detailed Analysis of Intrafraction Motion Using High-Frequency kV Imaging During Irradiation.

PURPOSE: Spine stereotactic body radiation therapy (SBRT) requires high positioning accuracy and a stable patient to maximize target coverage and reduce excessive irradiation to organs at risk. Positional verification during spine SBRT delivery helps to ensure accurate positioning for all patients. We report our experience with noninvasive 3-dimensional target position monitoring during volumetric modulated arc therapy of spine metastases in nonimmobilized patients positioned using only a thin mattress and simple arm and knee supports. METHODS AND MATERIALS: Fluoroscopic planar kV images were acquired at 7 frames/s using the on-board imaging system during volumetric modulated arc therapy spine SBRT. Template matching and triangulation were used to track the target in vertical, longitudinal, and lateral directions. If the tracking trace deviated >1 mm from the planned position in ≥1 direction, treatment was manually interrupted and 6-dimensional cone beam computed tomography (CBCT)-based couch correction was performed. Tracking data were used to retrospectively analyze the target position. Positional data, agreement with CBCT, correlation between position of the couch and direction of any positional correction, and treatment times were analyzed. RESULTS: In total, 175 fractions were analyzed. Delivery was interrupted 83 times in 66 fractions for a deviation >1 mm. In 97% of cases the difference between tracking data and subsequent clinical shift performed after the CBCT match was ≤0.5 mm. Lateral/longitudinal shift performed after intervention correlated with the couch roll/pitch at the start of treatment (correlation coefficient, -0.63/0.53). Mean (SD; range) time between start of first imaging and end of the last arc was 15.2 minutes (5.1; 7.6-36.3). CONCLUSIONS: Spine tracking during irradiation can be used to prompt an intervention CBCT scan and repositioning so that a spine SBRT target deviates by ≤1 mm from the planned position, even in nonimmobilized patients. kV tracking and CBCT are in good agreement. The data support verification CBCT after all 6 degrees-of-freedom positional corrections in nonimmobilized spine SBRT patients.

Dose distribution prediction for head-and-neck cancer radiotherapy using a generative adversarial network: influence of input data.

Purpose: A three-dimensional deep generative adversarial network (GAN) was used to predict dose distributions for locally advanced head and neck cancer radiotherapy. Given the labor- and time-intensive nature of manual planning target volume (PTV) and organ-at-risk (OAR) segmentation, we investigated whether dose distributions could be predicted without the need for fully segmented datasets. Materials and methods: GANs were trained/validated/tested using 320/30/35 previously segmented CT datasets and treatment plans. The following input combinations were used to train and test the models: CT-scan only (C); CT+PTVboost/elective (CP); CT+PTVs+OARs+body structure (CPOB); PTVs+OARs+body structure (POB); PTVs+body structure (PB). Mean absolute errors (MAEs) for the predicted dose distribution and mean doses to individual OARs (individual salivary glands, individual swallowing structures) were analyzed. Results: For the five models listed, MAEs were 7.3 Gy, 3.5 Gy, 3.4 Gy, 3.4 Gy, and 3.5 Gy, respectively, without significant differences among CP-CPOB, CP-POB, CP-PB, among CPOB-POB. Dose volume histograms showed that all four models that included PTV contours predicted dose distributions that had a high level of agreement with clinical treatment plans. The best model CPOB and the worst model PB (except model C) predicted mean dose to within ±3 Gy of the clinical dose, for 82.6%/88.6%/82.9% and 71.4%/67.1%/72.2% of all OARs, parotid glands (PG), and submandibular glands (SMG), respectively. The R2 values (0.17/0.96/0.97/0.95/0.95) of OAR mean doses for each model also indicated that except for model C, the predictions correlated highly with the clinical dose distributions. Interestingly model C could reasonably predict the dose in eight patients, but on average, it performed inadequately. Conclusion: We demonstrated the influence of the CT scan, and PTV and OAR contours on dose prediction. Model CP was not statistically different from model CPOB and represents the minimum data statistically required to adequately predict the clinical dose distribution in a group of patients.

Deep learning-based markerless lung tumor tracking in stereotactic radiotherapy using Siamese networks.

BACKGROUND: Radiotherapy (RT) is involved in about 50% of all cancer patients, making it a very important treatment modality. The most common type of RT is external beam RT, which consists of delivering the radiation to the tumor from outside the body. One novel treatment delivery method is volumetric modulated arc therapy (VMAT), where the gantry continuously rotates around the patient during the radiation delivery. PURPOSE: Accurate tumor position monitoring during stereotactic body radiotherapy (SBRT) for lung tumors can help to ensure that the tumor is only irradiated when it is inside the planning target volume. This can maximize tumor control and reduce uncertainty margins, lowering organ-at-risk dose. Conventional tracking methods are prone to errors, or have a low tracking rate, especially for small tumors that are in close vicinity to bony structures. METHODS: We investigated patient-specific deep Siamese networks for real-time tumor tracking, during VMAT. Due to lack of ground truth tumor locations in the kilovoltage (kV) images, each patient-specific model was trained on synthetic data (DRRs), generated from the 4D planning CT scans, and evaluated on clinical data (x-rays). Since there are no annotated datasets with kV images, we evaluated the model on a 3D printed anthropomorphic phantom but also on six patients by computing the correlation coefficient with the breathing-related vertical displacement of the surface-mounted marker (RPM). For each patient/phantom, we used 80% of DRRs for training and 20% for validation. RESULTS: The proposed Siamese model outperformed the conventional benchmark template matching-based method (RTR): (1) when evaluating both methods on the 3D phantom, the Siamese model obtained a 0.57-0.79-mm mean absolute distance to the ground truth tumor locations, compared to 1.04-1.56 mm obtained by RTR; (2) on patient data, the Siamese-determined longitudinal tumor position had a correlation coefficient of 0.71-0.98 with the RPM, compared to 0.07-0.85 for RTR; (3) the Siamese model had a 100% tracking rate, compared to 62%-82% for RTR. CONCLUSIONS: Based on these results, we argue that Siamese-based real-time 2D markerless tumor tracking during radiation delivery is possible. Further investigation and development of 3D tracking is warranted.

Single Ultra-High Dose Rate Proton Transmission Beam for Whole Breast FLASH-Irradiation: Quantification of FLASH-Dose and Relation with Beam Parameters.

Healthy tissue-sparing effects of FLASH (≥40 Gy/s, ≥4-8 Gy/fraction) radiotherapy (RT) make it potentially useful for whole breast irradiation (WBI), since there is often a lot of normal tissue within the planning target volume (PTV). We investigated WBI plan quality and determined FLASH-dose for various machine settings using ultra-high dose rate (UHDR) proton transmission beams (TBs). While five-fraction WBI is commonplace, a potential FLASH-effect might facilitate shorter treatments, so hypothetical 2- and 1-fraction schedules were also analyzed. Using one tangential 250 MeV TB delivering 5 × 5.7 Gy, 2 × 9.74 Gy or 1 × 14.32 Gy, we evaluated: (1) spots with equal monitor units (MUs) in a uniform square grid with variable spacing; (2) spot MUs optimized with a minimum MU-threshold; and (3) splitting the optimized TB into two sub-beams: one delivering spots above an MU-threshold, i.e., at UHDRs; the other delivering the remaining spots necessary to improve plan quality. Scenarios 1-3 were planned for a test case, and scenario 3 was also planned for three other patients. Dose rates were calculated using the pencil beam scanning dose rate and the sliding-window dose rate. Various machine parameters were considered: minimum spot irradiation time (minST): 2 ms/1 ms/0.5 ms; maximum nozzle current (maxN): 200 nA/400 nA/800 nA; two gantry-current (GC) techniques: energy-layer and spot-based. For the test case (PTV = 819 cc) we found: (1) a 7 mm grid achieved the best balance between plan quality and FLASH-dose for equal-MU spots; (2) near the target boundary, lower-MU spots are necessary for homogeneity but decrease FLASH-dose; (3) the non-split beam achieved >95% FLASH for favorable (not clinically available) machine parameters (SB GC, low minST, high maxN), but <5% for clinically available settings (EB GC, minST = 2 ms, maxN = 200 nA); and (4) splitting gave better plan quality and higher FLASH-dose (~50%) for available settings. The clinical cases achieved ~50% (PTV = 1047 cc) or >95% (PTV = 477/677 cc) FLASH after splitting. A single UHDR-TB for WBI can achieve acceptable plan quality. Current machine parameters limit FLASH-dose, which can be partially overcome using beam-splitting. WBI FLASH-RT is technically feasible.

Impact of imperfection in medical imaging data on deep learning-based segmentation performance: An experimental study using synthesized data.

BACKGROUND: Clinical data used to train deep learning models are often not clean data. They can contain imperfections in both the imaging data and the corresponding segmentations. PURPOSE: This study investigates the influence of data imperfections on the performance of deep learning models for parotid gland segmentation. This was done in a controlled manner by using synthesized data. The insights this study provides may be used to make deep learning models better and more reliable. METHODS: The data were synthesized by using the clinical segmentations, creating a pseudo ground-truth in the process. Three kinds of imperfections were simulated: incorrect segmentations, low image contrast, and artifacts in the imaging data. The severity of each imperfection was varied in five levels. Models resulting from training sets from each of the five levels were cross-evaluated with test sets from each of the five levels. RESULTS: Using synthesized data led to almost perfect parotid gland segmentation when no error was added. Lowering the quality of the parotid gland segmentations used for training substantially lowered the model performance. Additionally, lowering the image quality of the training data by decreasing the contrast or introducing artifacts made the resulting models more robust to data containing those respective kinds of data imperfection. CONCLUSION: This study demonstrated the importance of good-quality segmentations for deep learning training and it shows that using low-quality imaging data for training can enhance the robustness of the resulting models.

Same-day adaptive palliative radiotherapy without prior CT simulation: Early outcomes in the FAST-METS study.

BACKGROUND AND PURPOSE: Standard palliative radiotherapy workflows involve waiting times or multiple clinic visits. We developed and implemented a rapid palliative workflow using diagnostic imaging (dCT) for pre-planning, with subsequent on-couch target and plan adaptation based on a synthetic computed tomography (CT) obtained from cone-beam CT imaging (CBCT). MATERIALS AND METHODS: Patients with painful bone metastases and recent diagnostic imaging were eligible for inclusion in this prospective, ethics-approved study. The workflow consisted of 1) telephone consultation with a radiation oncologist (RO); 2) pre-planning on the dCT using planning templates and mostly intensity-modulated radiotherapy; 3) RO consultation on the day of treatment; 4) CBCT scan with on-couch adaptation of the target and treatment plan; 5) delivery of either scheduled or adapted treatment plan. Primary outcomes were dosimetric data and treatment times; secondary outcome was patient satisfaction. RESULTS: 47 patients were enrolled between December 2021 and October 2022. In all treatments, adapted treatment plans were chosen due to significant improvements in target coverage (PTV/CTV V95%, p-value < 0.005) compared to the original treatment plan calculated on daily anatomy. Most patients were satisfied with the workflow. The average treatment time, including consultation and on-couch adaptive treatment, was 85 minutes. On-couch adaptation took on average 30 min. but was longer in cases where the automated deformable image registration failed to correctly propagate the targets. CONCLUSION: A fast treatment workflow for patients referred for painful bone metastases was implemented successfully using online adaptive radiotherapy, without a dedicated CT simulation. Patients were generally satisfied with the palliative radiotherapy workflow.

Evaluation of a workflow for cone-beam CT-guided online adaptive palliative radiotherapy planned using diagnostic CT scans.

PURPOSE: Single-visit radiotherapy (RT) is beneficial for patients requiring pain control and can limit interruptions to systemic treatments. However, the requirement for a dedicated planning CT (pCT)-scan can result in treatment delays. We developed a workflow involving preplanning on available diagnostic CT (dCT) imaging, followed by online plan adaption using a cone-beam CT (CBCT)-scan prior to RT-delivery, in order to account for any changes in anatomy and target position. METHODS: Patients previously treated with palliative RT for bone metastases were selected from our hospital database. Patient dCT-images were deformed to treatment CBCTs in the Ethos platform (Varian Medical Systems) and a synthetic CT (sCT) generated. Treatment quality was analyzed by comparing a coverage of the V95% of the planning/clinical target volume and different organ-at-risk (OAR) doses between adapted and initial clinical treatment plans. Doses were recalculated on the CBCT and sCT in a separate treatment planning system. Adapted plan doses were measured on-couch using an anthropomorphic phantom with a Gafchromic EBT3 dosimetric film and compared to dose calculations. RESULTS: All adapted treatment plans met the clinical goals for target and OARs and outperformed the original treatment plans calculated on the (daily) sCT. Differences in V95% of the target volume coverage between the initial and adapted treatments were <0.2%. Dose recalculations on CBCT and sCT were comparable, and the average gamma pass rate (3%/2 mm) of dosimetric measurements was 98.8%. CONCLUSIONS: Online daily adaptive RT using dCTs instead of a dedicated pCT is feasible using the Ethos platform. This workflow has now been implemented clinically.

Deep Learning for Automated Elective Lymph Node Level Segmentation for Head and Neck Cancer Radiotherapy.

Depending on the clinical situation, different combinations of lymph node (LN) levels define the elective LN target volume in head-and-neck cancer (HNC) radiotherapy. The accurate auto-contouring of individual LN levels could reduce the burden and variability of manual segmentation and be used regardless of the primary tumor location. We evaluated three deep learning approaches for the segmenting individual LN levels I−V, which were manually contoured on CT scans from 70 HNC patients. The networks were trained and evaluated using five-fold cross-validation and ensemble learning for 60 patients with (1) 3D patch-based UNets, (2) multi-view (MV) voxel classification networks and (3) sequential UNet+MV. The performances were evaluated using Dice similarity coefficients (DSC) for automated and manual segmentations for individual levels, and the planning target volumes were extrapolated from the combined levels I−V and II−IV, both for the cross-validation and for an independent test set of 10 patients. The median DSC were 0.80, 0.66 and 0.82 for UNet, MV and UNet+MV, respectively. Overall, UNet+MV significantly (p < 0.0001) outperformed other arrangements and yielded DSC = 0.87, 0.85, 0.86, 0.82, 0.77, 0.77 for the combined and individual level I−V structures, respectively. Both PTVs were also significantly (p < 0.0001) more accurate with UNet+MV, with DSC = 0.91 and 0.90, respectively. The accurate segmentation of individual LN levels I−V can be achieved using an ensemble of UNets. UNet+MV can further refine this result.

Influence of Beam Angle on Normal Tissue Complication Probability of Knowledge-Based Head and Neck Cancer Proton Planning.

Knowledge-based planning solutions have brought significant improvements in treatment planning. However, the performance of a proton-specific knowledge-based planning model in creating knowledge-based plans (KBPs) with beam angles differing from those used to train the model remains unexplored. We used a previously validated RapidPlanPT model and scripting to create nine KBPs, one with default and eight with altered beam angles, for 10 recent oropharynx cancer patients. The altered-angle plans were compared against the default-angle ones in terms of grade 2 dysphagia and xerostomia normal tissue complication probability (NTCP), mean doses of several organs at risk, and dose homogeneity index (HI). As KBP could be suboptimal, a proof of principle automatic iterative optimizer (AIO) was added with the aim of reducing the plan NTCP. There were no statistically significant differences in NTCP or HI between default- and altered-angle KBPs, and the altered-angle plans showed a <1% reduction in NTCP. AIO was able to reduce the sum of grade 2 NTCPs in 66/90 cases with mean a reduction of 3.5 ± 1.8%. While the altered-angle plans saw greater benefit from AIO, both default- and altered-angle plans could be improved, indicating that the KBP model alone was not completely optimal to achieve the lowest NTCP. Overall, the data showed that the model was robust to the various beam arrangements within the range described in this analysis.

Single-fraction 34 Gy Lung Stereotactic Body Radiation Therapy Using Proton Transmission Beams: FLASH-dose Calculations and the Influence of Different Dose-rate Methods and Dose/Dose-rate Thresholds.

Purpose: Research suggests that in addition to the dose-rate, a dose threshold is also important for the reduction in normal tissue toxicity with similar tumor control after ultrahigh dose-rate radiation therapy (UHDR-RT). In this analysis we aimed to identify factors that might limit the ability to achieve this "FLASH"-effect in a scenario attractive for UHDR-RT (high fractional beam dose, small target, few organs-at-risk): single-fraction 34 Gy lung stereotactic body radiation therapy. Methods and Materials: Clinical volumetric-modulated arc therapy (VMAT) plans, intensity modulated proton therapy (IMPT) plans and transmission beam (TB) plans were compared for 6 small and 1 large lung lesion. The TB-plan dose-rate was calculated using 4 methods and the FLASH-percentage (percentage of dose delivered at dose-rates ≥40/100 Gy/s and ≥4/8 Gy) was determined for various variables: a minimum spot time (minST) of 0.5/2 ms, maximum nozzle current (maxN) of 200/40 0nA, and 2 gantry current (GC) techniques (energy-layer based, spot-based [SB]). Results: Based on absolute doses 5-beam TB and VMAT-plans are similar, but TB-plans have higher rib, skin, and ipsilateral lung dose than IMPT. Dose-rate calculation methods not considering scanning achieve FLASH-percentages between ∼30% to 80%, while methods considering scanning often achieve <30%. FLASH-percentages increase for lower minST/higher maxN and when using SB GC instead of energy-layer based GC, often approaching the percentage of dose exceeding the dose-threshold. For the small lesions average beam irradiation times (including scanning) varied between 0.06 to 0.31 seconds and total irradiation times between 0.28 to 1.57 seconds, for the large lesion beam times were between 0.16 to 1.47 seconds with total irradiation times of 1.09 to 5.89 seconds. Conclusions: In a theoretically advantageous scenario for FLASH we found that TB-plan dosimetry was similar to that of VMAT, but inferior to that of IMPT, and that decreasing minST or using SB GC increase the estimated amount of FLASH. For the appropriate machine/delivery parameters high enough dose-rates can be achieved regardless of calculation method, meaning that a possible FLASH dose-threshold will likely be the primary limiting factor.

Fast, Automated, Knowledge-Based Treatment Planning for Selecting Patients for Proton Therapy Based on Normal Tissue Complication Probabilities.

Purpose: Selecting patients who will benefit from proton therapy is laborious and subjective. We demonstrate a novel automated solution for creating high-quality knowledge-based plans (KBPs) using proton and photon beams to identify patients for proton treatment based on their normal tissue complication probabilities (NTCP). Methods and Materials: Two previously validated RapidPlan PT models for locally advanced head and neck cancer were used in combination with scripting to automatically create proton and photon KBPs for 72 patients with recent oropharynx cancer. NTCPs were calculated for each patient based on the KBPs, and patient selection was simulated according to the current Dutch national protocol. Results: The photon/proton KBP exhibited good correlation between predicted and achieved organ-at-risk mean doses, with a ≤5 Gy difference in 208/196 out of 215 structures relevant for the head and neck cancer NTCP model. The proton KBPs yielded on average 7.1/6.1/7.6 Gy lower dose to salivary/swallowing structures/oral cavity than the photon KBPs. This reduced average grade 2/3 dysphagia and xerostomia by 7.1/3.3 and 5.5/2.0 percentage points, resulting in 16 of 72 patients (22%) being indicated for proton treatment. The entire automated process took <30 minutes per patient. Conclusions: Automated support for decision making using KBP is feasible and fast. The planning solution has potential to speed up the planning and patient-selection process significantly without major compromises to the plan quality.

Determining Planning Priorities for SABR for Oligometastatic Disease: A Secondary Analysis of the SABR-COMET Phase II Randomized Trial.

PURPOSE: SABR may improve survival in patients with oligometastases, but for some lesions, safe delivery of SABR may require a reduction in delivered dose or target coverage. This study assessed the association between target coverage compromise and oncologic and survival outcomes. METHODS AND MATERIALS: Patients with a controlled primary malignancy and 1 to 5 oligometastases were randomized (1:2) between standard of care (SOC) treatment and SOC plus SABR. In patients receiving SABR, the target dose coverage was reduced to meet organ at risk (OAR) constraints, if necessary. The D99 value (minimum dose received by the hottest 99% of the planning target volume [PTV]) was used as a measure of PTV coverage for each treatment plan, and the relationship between the coverage compromise index (CCI, defined as D99/prescription dose) and patient outcomes was assessed. RESULTS: Sixty-two patients in the SABR arm had dosimetric information available and a total of 109 lesions were evaluated. The mean CCI per lesion was 0.96 (95% CI, 0.56-1.61). Of the 109 lesions evaluated, 29.4% (n = 32) required coverage compromise (CCI <0.9). Adrenal metastases required coverage compromise in 100% of analyzed lesions (n = 7). CCI was not significantly associated with lesional control, adverse events, overall survival (OS), or progression-free survival (PFS). CONCLUSIONS: Target compromise was required in a substantial minority of cases, but PTV coverage was not associated with OS, progression-free survival, or lesional control. This suggests that OAR constraints used for SABR treatments in the oligometastatic setting should continue to be prioritized during planning.

The markerless lung target tracking AAPM Grand Challenge (MATCH) results.

PURPOSE: Lung stereotactic ablative body radiotherapy (SABR) is a radiation therapy success story with level 1 evidence demonstrating its efficacy. To provide real-time respiratory motion management for lung SABR, several commercial and preclinical markerless lung target tracking (MLTT) approaches have been developed. However, these approaches have yet to be benchmarked using a common measurement methodology. This knowledge gap motivated the MArkerless lung target Tracking CHallenge (MATCH). The aim was to localize lung targets accurately and precisely in a retrospective in silico study and a prospective experimental study. METHODS: MATCH was an American Association of Physicists in Medicine sponsored Grand Challenge. Common materials for the in silico and experimental studies were the experiment setup including an anthropomorphic thorax phantom with two targets within the lungs, and a lung SABR planning protocol. The phantom was moved rigidly with patient-measured lung target motion traces, which also acted as ground truth motion. In the retrospective in silico study a volumetric modulated arc therapy treatment was simulated and a dataset consisting of treatment planning data and intra-treatment kilovoltage (kV) and megavoltage (MV) images for four blinded lung motion traces was provided to the participants. The participants used their MLTT approach to localize the moving target based on the dataset. In the experimental study, the participants received the phantom experiment setup and five patient-measured lung motion traces. The participants used their MLTT approach to localize the moving target during an experimental SABR phantom treatment. The challenge was open to any participant, and participants could complete either one or both parts of the challenge. For both the in silico and experimental studies the MLTT results were analyzed and ranked using the prospectively defined metric of the percentage of the tracked target position being within 2 mm of the ground truth. RESULTS: A total of 30 institutions registered and 15 result submissions were received, four for the in silico study and 11 for the experimental study. The participating MLTT approaches were: Accuray CyberKnife (2), Accuray Radixact (2), BrainLab Vero, C-RAD, and preclinical MLTT (5) on a conventional linear accelerator (Varian TrueBeam). For the in silico study the percentage of the 3D tracking error within 2 mm ranged from 50% to 92%. For the experimental study, the percentage of the 3D tracking error within 2 mm ranged from 39% to 96%. CONCLUSIONS: A common methodology for measuring the accuracy of MLTT approaches has been developed and used to benchmark preclinical and commercial approaches retrospectively and prospectively. Several MLTT approaches were able to track the target with sub-millimeter accuracy and precision. The study outcome paves the way for broader clinical implementation of MLTT. MATCH is live, with datasets and analysis software being available online at https://www.aapm.org/GrandChallenge/MATCH/ to support future research.

Relationship between Treatment Plan Dosimetry, Toxicity, and Survival following Intensity-Modulated Radiotherapy, with or without Chemotherapy, for Stage III Inoperable Non-Small Cell Lung Cancer.

Concurrent chemoradiotherapy (cCRT) is the preferred treatment for stage III NSCLC because surgery containing multimodality treatment is often not appropriate. Alternatives, often for less fit patients, include sequential CRT and RT alone. Many reports describing the relationship between overall survival (OS), toxicity, and dosimetry are based on clinical trials, with strict criteria for patient selection. We performed an institutional analysis to study the relationship between dosimetric parameters, toxicity, and OS in inoperable patients with stage III NSCLC treated with (hybrid) IMRT/VMAT-based techniques in routine clinical practice. Eligible patients had undergone treatment with radical intent using cCRT, sCRT, or RT alone, planned to a total dose ≥ 50 Gy delivered in ≥15 fractions. All analyses were performed for two patient groups, (1) cCRT (n = 64) and (2) sCRT/RT (n = 65). The toxicity rate differences between the two groups were not significant, and OS was 29 and 17 months, respectively. For sCRT/RT, no dosimetric factors were associated with OS, whereas for cCRT, PTV-volume, esophagus V50 Gy, and contralateral lung V5 Gy were associated. cCRT OS was significantly lower in patients with esophagitis ≥ G2. The overall rate of ≥G3 pneumonitis was low (3%), and the rate of high-grade esophagitis the OS in this real-world patient population was comparable to those reported in clinical trials. Based on this hypothesis-generating data, more aggressive esophageal sparing merits consideration. Institutional auditing and benchmarking of the planning strategy, dosimetry, and outcome have an important role to play in the continuous quality improvement process.

Markerless 3D tumor tracking during single-fraction free-breathing 10MV flattening-filter-free stereotactic lung radiotherapy.

BACKGROUND AND PURPOSE: Positional verification during single fraction lung SBRT could increase confidence and reduce the chance of geographic miss. As planar 2DkV imaging during VMAT irradiation is already available on current linear accelerators, markerless tracking based on these images could offer widely available and low-cost verification. We evaluated treatment delivery data and template matching and triangulation for 3D-positional verification during free-breathing, single fraction (34 Gy), 10 MV flattening-filter-free VMAT lung SBRT. METHODS AND MATERIALS: Tumor tracking based on kV imaging at 7 frames/second was performed during irradiation in 6 consecutive patients (7 lesions). Tumor characteristics, tracking ability, comparison of tracking displacements with CBCT-based shifts, tumor position relative to the PTV margin, and treatment times are reported. RESULTS: For all 7 lesions combined, 3D tumor position could be determined for, on average, 71% (51-84%) of the total irradiation time. Visually estimated tracked and automated match +/- manually-corrected CBCT-derived displacements generally agreed within 1 mm. During the tracked period, the longitudinal, lateral and vertical position of the tumor was within a 5 mm/3 mm PTV margin 95.5/85.3% of the time. The PTV was derived from the ITV including all tumor motion. The total time from first set-up imaging to end of the last arc was 18.3-31.4 min (mean = 23.4, SD = 4.1). CONCLUSION: 3D positional verification during irradiation of small lung targets with limited motion, was feasible. However, tumor position could not be determined for on average 29% of the time. Improvements are needed. Margin reduction may be feasible. Imaging and delivery of a single 34 Gy fraction was fast.

The Impact of the Availability of Immunotherapy on Patterns of Care in Stage III NSCLC: A Dutch Multicenter Analysis.

INTRODUCTION: Treatment patterns in stage III NSCLC can vary considerably between countries. The PACIFIC trial reported improvements in progression-free and overall survival with adjuvant durvalumab after concurrent chemoradiotherapy (CCRT). We studied treatment decision-making by three Dutch regional thoracic multidisciplinary tumor boards between 2015 and 2019, to identify changes in practice when adjuvant durvalumab became available. METHODS: Details of patients presenting with stage III NSCLC were retrospectively collected. Both CCRT and multimodality schemes incorporating planned surgery were defined as being radical-intent treatment (RIT). RESULTS: Of 855 eligible patients, most (95%) were discussed at a thoracic multidisciplinary tumor board, which recommended a RIT in 63% (n = 510). Only 52% (n = 424) of the patients finally received a RIT. Predictors for not recommending RIT were age greater than or equal to 70 years, WHO performance score greater than or equal to 2, Charlson comorbidity index greater than or equal to 2 (excluding age), forced expiratory volume in 1 second less than 80% of predicted value, N3 disease, and period of diagnosis. Between 2015 to 2017 and 2018 to 2019, the proportion of patients undergoing CCRT increased from 34% to 42% (p = 0.02) and use of sequential chemoradiotherapy declined (21%-16%, p = 0.05). Rates of early toxicity and 1-year mortality were comparable for both periods. After 2018, 57% of the patients who underwent CCRT (90 of 159) received adjuvant durvalumab. CONCLUSIONS: After publication of the PACIFIC trial, a significant increase was observed in the use of CCRT for patients with stage III NSCLC with rates of early toxicity and mortality being unchanged. Since 2018, 57% of the patients undergoing CCRT went on to receive adjuvant durvalumab. Nevertheless, approximately half of the patients were still considered unfit for a RIT.

National Protocol for Model-Based Selection for Proton Therapy in Head and Neck Cancer.

In the Netherlands, the model-based approach is used to identify patients with head and neck cancer who may benefit most from proton therapy in terms of prevention of late radiation-induced side effects in comparison with photon therapy. To this purpose, a National Indication Protocol Proton therapy for Head and Neck Cancer patients (NIPP-HNC) was developed, which has been approved by the health care authorities. When patients qualify according to the guidelines of the NIPP-HNC, proton therapy is fully reimbursed. This article describes the procedures that were followed to develop this NIPP-HNC and provides all necessary information to introduce model-based selection for patients with head and neck cancer into routine clinical practice.

Markerless Real-Time 3-Dimensional kV Tracking of Lung Tumors During Free Breathing Stereotactic Radiation Therapy.

PURPOSE: Accurate verification of tumor position during irradiation could reduce the probability of target miss. We investigated whether a commercial gantry-mounted 2-dimensional (2D) kilo-voltage (kV) imaging system could be used for real-time 3D tumor tracking during volumetric modulated arc therapy (VMAT) lung stereotactic body radiation therapy (SBRT). Markerless tumor tracking on kV fluoroscopic images was validated using a life-like moving thorax phantom and subsequently performed on kV images continuously acquired before and during free-breathing VMAT lung SBRT. METHODS AND MATERIALS: The 3D-printed/molded phantom containing 3 lung tumors was moved in 3D in TrueBeam developer mode, using simulated regular/irregular breathing patterns. Planar kV images were acquired at 7 frames/s during 11 Gy/fraction 10 MV flattening filter free VMAT. 2D reference templates were created for each gantry angle using the planning 4D computed tomography inspiration phase. kV images and templates were matched using normalized cross correlation to determine 2D tumor position, and triangulation of 2D matched projections determined the third dimension. 3D target tracking performed on cone beam computed tomography projection data from 18 patients (20 tumors) and real-time online tracking data from 2 of the 18 patients who underwent free-breathing VMAT lung SBRT are presented. RESULTS: For target 1 and 2 of the phantom (upper lung and middle/medial lung, mean density -130 Hounsfield units), 3D results within 2 mm of the known position were present in 92% and 96% of the kV projections, respectively. For target 3 (inferior lung, mean density -478 Hounsfield units) this dropped to 80%. Benchmarking against the respiratory signal, 13/20 (65%) tumors (10.5 ± 11.1 cm3) were considered successfully tracked on the cone beam computed tomography data. Tracking was less successful (≤50% of the time) in 7/20 (1.2 ± 1.5 cm3). Successful online tracking during lung SBRT was demonstrated. CONCLUSIONS: 3D markerless tumor tracking on a standard linear accelerator using template matching and triangulation of free-breathing kV fluoroscopic images was possible in 65% of small lung tumors. The smallest tumors were most challenging.