A multicentric randomized controlled phase III trial of adaptive and 18F-FDG-PET-guided dose-redistribution in locally advanced head and neck squamous cell carcinoma (ARTFORCE)

BACKGROUND AND PURPOSE

This multicenter randomized phase III trial evaluated whether locoregional control of patients with LAHNSCC could be improved by fluorodeoxyglucose-positron emission tomography (FDG-PET)-guided dose-escalation while minimizing the risk of increasing toxicity using a dose-redistribution and scheduled adaptation strategy.

MATERIALS AND METHODS

Patients with T3-4-N0-3-M0 LAHNSCC were randomly assigned (1:1) to either receive a dose distribution ranging from 64-84 Gy/35 fractions with adaptation at the 10thfraction (rRT) or conventional 70 Gy/35 fractions (cRT). Both arms received concurrent three-cycle 100 mg/m2cisplatin. Primary endpoints were 2-year locoregional control (LRC) and toxicity. Primary analysis was based on the intention-to-treat principle.

RESULTS

Due to slow accrual, the study was prematurely closed (at 84 %) after randomizing 221 eligible patients between 2012 and 2019 to receive rRT (N = 109) or cRT (N = 112). The 2-year LRC estimate difference of 81 % (95 %CI 74-89 %) vs. 74 % (66-83 %) in the rRT and cRT arm, respectively, was not found statistically significant (HR 0.75, 95 %CI 0.43-1.31,P=.31). Toxicity prevalence and incidence rates were similar between trial arms, with exception for a significant increased grade ≥ 3 pharyngolaryngeal stenoses incidence rate in the rRT arm (0 versus 4 %,P=.05). In post-hoc subgroup analyses, rRT improved LRC for patients with N0-1 disease (HR 0.21, 95 %CI 0.05-0.93) and oropharyngeal cancer (0.31, 0.10-0.95), regardless of HPV.

CONCLUSION

Adaptive and dose redistributed radiotherapy enabled dose-escalation with similar toxicity rates compared to conventional radiotherapy. While FDG-PET-guided dose-escalation did overall not lead to significant tumor control or survival improvements, post-hoc results showed improved locoregional control for patients with N0-1 disease or oropharyngeal cancer treated with rRT.

Dynamic recurrent inference machines for accelerated MRI-guided radiotherapy of the liver

Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2-weighted acquisitions. We demonstrate with an ablation study that the DRIM outperforms the RIM, increasing the SSIM score from about 0.89 to 0.95. The DRIM allowed for an approximately 2.7 times faster scan time than the current clinical protocol with only a slight loss in image sharpness. Correlations between slice locations can also be used, but were found to be of less importance, as were a majority of tested variations in network architecture, as long as the respiratory states are processed by the network. Through cross-validation, the DRIM is also shown to be robust in terms of training data. We further demonstrate a good performance across a large range of subsampling factors, and conclude through an evaluation by a radiation oncologist that reconstructed images of the liver contour and inner structures are of a clinically acceptable standard at acceleration factors 10x and 8x, respectively. Finally, we show that binning the data with respect to respiratory states prior to reconstruction comes at a slight cost to reconstruction quality, but at greater speed of the overall protocol.

A deep learning based dynamic arc radiotherapy photon dose engine trained on Monte Carlo dose distributions

Background and purpose

Despite hardware acceleration, state-of-the-art Monte Carlo (MC) dose engines require considerable computation time to reduce stochastic noise. We developed a deep learning (DL) based dose engine reaching high accuracy at strongly reduced computation times.

Materials and methods

Radiotherapy treatment plans and computed tomography scans were collected for 350 treatments in a variety of tumor sites. Dose distributions were computed using a MC dose engine for ∼ 30,000 separate segments at 6 MV and 10 MV beam energies, both flattened and flattening filter free. For dynamic arcs these explicitly incorporated the leaf, jaw and gantry motions during dose delivery. A neural network was developed, combining two-dimensional convolution and recurrence using 64 hidden channels. Parameters were trained to minimize the mean squared log error loss between the MC computed dose and the model output. Full dose distributions were reconstructed for 100 additional treatment plans. Gamma analyses were performed to assess accuracy.

Results

DL dose evaluation was on average 82 times faster than MC computation at a 1 % accuracy setting. In voxels receiving at least 10 % of the maximum dose the overall global gamma pass rate using a 2 % and 2 mm criterion was 99.6 %, while mean local gamma values were accurate within 2 %. In the high dose region over 50 % of maximum the mean local gamma approached a 1 % accuracy.

Conclusions

A DL based dose engine was implemented, able to accurately reproduce MC computed dynamic arc radiotherapy dose distributions at high speed.

On retrospective k-space subsampling schemes for deep MRI reconstruction

Acquiring fully-sampled MRI k-space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, this often results in imprecise reconstructions, even with the use of Deep Learning (DL), especially at high acceleration factors. Non-rectilinear or non-Cartesian trajectories can be implemented in MRI scanners as alternative subsampling options. This work investigates the impact of the k-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models. The Recurrent Variational Network (RecurrentVarNet) was used as the DL-based MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil k-space measurements from three datasets were retrospectively subsampled with different accelerations using eight distinct subsampling schemes: four Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian. Experiments were conducted in two frameworks: scheme-specific, where a distinct model was trained and evaluated for each dataset-subsampling scheme pair, and multi-scheme, where for each dataset a single model was trained on data randomly subsampled by any of the eight schemes and evaluated on data subsampled by all schemes. In both frameworks, RecurrentVarNets trained and evaluated on non-rectilinearly subsampled data demonstrated superior performance, particularly for high accelerations. In the multi-scheme setting, reconstruction performance on rectilinearly subsampled data improved when compared to the scheme-specific experiments. Our findings demonstrate the potential for using DL-based methods, trained on non-rectilinearly subsampled measurements, to optimize scan time and image quality.

Baseline shift corrections towards the heart: External validation of the impact on survival in early-stage NSCLC patients

PURPOSE

To externally validate Johnson-Hart et al. findings: the association of tumor baseline shifts towards the heart with overall survival (OS) in SBRT for NSCLC. Further analysis included investigating the presence of interfractional heart baseline shifts and the association of OS with heart dose change during treatment.

METHODS

Data from 416 SBRT early-stage NSCLC patients was collected. Pearson's correlations (PCCs) between clinical variables and treatment-averaged tumor shifts towards/away from the heart were explored. Validation of published multivariable Cox model was performed. PCCs between heart and tumor baseline shifts were analyzed. Dose accumulation was performed following daily CBCT-to-pCT deformable registration. Maximum heart dose (D0) was computed for planned and accumulated doses. Differences in OS according to shifts towards/away from the heart or D0 increase/decrease were analyzed. Significant D0 differences between patients with D0 increase/decrease and different tumor locations were explored.

RESULTS

Tumor shifts towards/away from the heart showed no significant association with OS (p = 0.91). Distance between PTV and heart correlated significantly (PCC = 0.18) with shifts to the heart. Cox model did not validate in our cohort. Heart presented baseline shifts positively correlated with tumor baseline shifts in all three directions (PCC ≥ 0.38; p < 0.001). Counterintuitively, patients experiencing increased D0 during treatment showed significantly better OS (p = 0.0077). Upper-lobe tumor patients with increased D0 had lower D0 than those with decreased D0 (right-upper-lobe p ≤ 0.018).

CONCLUSIONS

In our SBRT cohort, the shifts towards the heart were not associated with worse OS. Moderate correlations were found between tumor and heart baseline shifts in each direction. Moreover, the distance between the PTV and the heart showed a significant correlation with shifts to the heart.

Impact of cone-beam computed tomography artifacts on dose calculation accuracy for lung cancer

BACKGROUND

To implement image-guided adaptive radiotherapy (IGART), many studies investigated dose calculations on cone-beam computed tomography (CBCT). A high HU accuracy is crucial for a high dose calculation accuracy and many imaging sites showed satisfactory results. It has been shown that the dose calculation accuracy for lung cancer lags behind.

PURPOSE

To examine why the dose calculation accuracy for lung is insufficient, the relative effects of the field-of-view (FOV), breathing motion, and scatter on dose calculation accuracy were studied.

METHODS

A framework was built to simulate CBCT scans for lung cancer patients by forward projecting repeat CT (rCT) scans for two scan geometries: small (SFOV) and medium FOV (MFOV). Breathing motion was modeled by applying a 4D deformation vector field to the mid-position rCT. Scatter was modeled by Monte-Carlo simulations with/without an anti-scatter grid (ASG). Simulated projections were reconstructed using filtered back-projection with/without scatter correction. In case of the SFOV, the CBCT images were patched with the planning CT scan in axial direction. The treatment plan was recalculated on the rCT and simulated CBCT. The mean Hounsfield unit (HU) difference (ΔHUmean ), the structural similarity index measure (SSIM), and γ metrics were calculated for the CBCT datasets of various imaging settings.

RESULTS

The differences in HU, SSIM and dose calculation accuracy for CBCTs with and without breathing motion were negligible (mean ΔHUmean  = 6.4 vs. 13.7, mean SSIM = 0.941 vs. 0.957, mean γ (ref = MFOV) = 0.75). The SFOV resulted in a lower HU (mean ΔHUmean  = -9.2 vs. 13.7) and SSIM (mean SSIM = 0.912 vs. 0.957), and therefore in dose differences compared to the MFOV (mean γ = 1.22). Scatter led to considerable discrepancies in all metrics. Adding only the ASG improved the results more than only applying a scatter correction algorithm. Combining ASG and scatter correction algorithm resulted in an even higher dose calculation accuracy.

CONCLUSIONS

Scatter and FOV are the main contributors to dose inaccuracies and motion has only a minor effect on dose calculation accuracy. Therefore, utilizing an appropriate scatter correction and FOV is important to achieve sufficient dose calculation accuracy to facilitate IGART for lung.

Stomach Motion and Deformation: Implications for Preoperative Gastric Cancer Radiation Therapy

PURPOSE

Selection and development of image guided strategies for preoperative gastric radiation therapy requires quantitative knowledge of the various sources of anatomic changes of the stomach. This study aims to investigate the magnitude of interfractional and intrafractional stomach motion and deformation using fiducial markers and 4-dimensional (4D) imaging.

METHODS AND MATERIALS

Fourteen patients who underwent preoperative gastric cancer radiation therapy received 2 to 6 fiducial markers distributed throughout the stomach (total of 54 markers) and additional imaging (ie, 1 planning 4D computed tomography [pCT], 20-25 pretreatment 4D cone beam [CB] CTs, 4-5 posttreatment 4D CBCTs). Marker coordinates on all end-exhale (EE) and end-inhale (EI) scans were obtained after a bony anatomy match. Interfractional marker displacements (ie, between EE pCT and all EE CBCTs) were evaluated for 5 anatomic regions (ie, cardia, small curvature, proximal and distal large curvature, and pylorus). Motion was defined as displacement of the center-of-mass of available markers (COMstomach), deformation as the average difference in marker-pair distances. Interfractional (ie, between EE pCT and all EE CBCTs), respiratory (between EE and EI pCT and CBCTs), and pre-post (pre- and posttreatment EE CBCTs) motion and deformation were quantified.

RESULTS

The interfractional marker displacement varied per anatomic region and direction, with systematic and random errors ranging from 1.6-8.8 mm and 2.2-8.2 mm, respectively. Respiratory motion varied per patient (median, 3-dimensional [3D] amplitude 5.2-20.0 mm) and day (interquartile range, 0.8-4.2 mm). Regarding COMstomach motion, respiratory motion was larger than interfractional motion (median, 10.9 vs 8.9 mm; P < .0001; Wilcoxon rank-sum), which was larger than pre-post motion (3.6 mm; P < .0001). Interfractional deformations (median, 5.8 mm) were significantly larger than pre-post deformations (2.6 mm; P < .0001), which were larger than respiratory deformation (1.8 mm; P < .0001).

CONCLUSIONS

The demonstrated sizable stomach motions and deformations during radiation therapy stress the need for generous nonuniform planning target volume margins for preoperative gastric cancer radiation therapy. These margins can be decreased by daily image guidance and adaptive radiation therapy.

Intrafraction Motion Management With MR-Guided Radiation Therapy

High quality radiation therapy requires highly accurate and precise dose delivery. MR-guided radiotherapy (MRgRT), integrating an MRI scanner with a linear accelerator, offers excellent quality images in the treatment room without subjecting patient to ionizing radiation. MRgRT therefore provides a powerful tool for intrafraction motion management. This paper summarizes different sources of intrafraction motion for different disease sites and describes the MR imaging techniques available to visualize and quantify intrafraction motion. It provides an overview of MR guided motion management strategies and of the current technical capabilities of the commercially available MRgRT systems. It describes how these motion management capabilities are currently being used in clinical studies, protocols and provides a future outlook.

End-to-end memory-efficient reconstruction for cone beam CT

BACKGROUND

Cone beam computed tomography (CBCT) plays an important role in many medical fields nowadays. Unfortunately, the potential of this imaging modality is hampered by lower image quality compared to the conventional CT, and producing accurate reconstructions remains challenging. A lot of recent research has been directed towards reconstruction methods relying on deep learning, which have shown great promise for various imaging modalities. However, practical application of deep learning to CBCT reconstruction is complicated by several issues, such as exceedingly high memory costs of deep learning methods when working with fully 3D data. Additionally, deep learning methods proposed in the literature are often trained and evaluated only on data from a specific region of interest, thus raising concerns about possible lack of generalization to other regions.

PURPOSE

In this work, we aim to address these limitations and propose LIRE: a learned invertible primal-dual iterative scheme for CBCT reconstruction.

METHODS

LIRE is a learned invertible primal-dual iterative scheme for CBCT reconstruction, wherein we employ a U-Net architecture in each primal block and a residual convolutional neural network (CNN) architecture in each dual block. Memory requirements of the network are substantially reduced while preserving its expressive power through a combination of invertible residual primal-dual blocks and patch-wise computations inside each of the blocks during both forward and backward pass. These techniques enable us to train on data with isotropic 2 mm voxel spacing, clinically-relevant projection count and detector panel resolution on current hardware with 24 GB video random access memory (VRAM).

RESULTS

Two LIRE models for small and for large field-of-view (FoV) setting were trained and validated on a set of 260 + 22 thorax CT scans and tested using a set of 142 thorax CT scans plus an out-of-distribution dataset of 79 head and neck CT scans. For both settings, our method surpasses the classical methods and the deep learning baselines on both test sets. On the thorax CT set, our method achieves peak signal-to-noise ratio (PSNR) of 33.84 ± 2.28 for the small FoV setting and 35.14 ± 2.69 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. On the head and neck CT set, our method achieves PSNR of 39.35 ± 1.75 for the small FoV setting and 41.21 ± 1.41 for the large FoV setting; U-Net baseline achieves PSNR of 33.08 ± 1.75 and 34.29 ± 2.71 respectively. Additionally, we demonstrate that LIRE can be finetuned to reconstruct high-resolution CBCT data with the same geometry but 1 mm voxel spacing and higher detector panel resolution, where it outperforms the U-Net baseline as well.

CONCLUSIONS

Learned invertible primal-dual schemes with additional memory optimizations can be trained to reconstruct CBCT volumes directly from the projection data with clinically-relevant geometry and resolution. Such methods can offer better reconstruction quality and generalization compared to classical deep learning baselines.

PC-Reg: A pyramidal prediction-correction approach for large deformation image registration

Deformable image registration plays an important role in medical image analysis. Deep neural networks such as VoxelMorph and TransMorph are fast, but limited to small deformations and face challenges in the presence of large deformations. To tackle large deformations in medical image registration, we propose PC-Reg, a pyramidal Prediction and Correction method for deformable registration, which treats multi-scale registration akin to solving an ordinary differential equation (ODE) across scales. Starting with a zero-initialized deformation at the coarse level, PC-Reg follows the predictor-corrector regime and progressively predicts a residual flow and a correction flow to update the deformation vector field through different scales. The prediction in each scale can be regarded as a single step of ODE integration. PC-Reg can be easily extended to diffeomorphic registration and is able to alleviate the multiscale accumulated upsampling and diffeomorphic integration error. Further, to transfer details from full resolution to low scale, we introduce a distillation loss, where the output is used as the target label for intermediate outputs. Experiments on inter-patient deformable registration show that the proposed method significantly improves registration not only for large but also for small deformations.

Severe radiation-induced lymphopenia during concurrent chemoradiotherapy for stage III non-small cell lung cancer: external validation of two prediction models

Background

Severe radiation-induced lymphopenia (RIL) in patients undergoing chemoradiotherapy (CRT) for non-small cell lung cancer (NSCLC) is associated with decreased immunotherapy efficacy and survival. At The Christie and MD Anderson Cancer Center (MDACC), prediction models for lymphopenia were developed in lung and esophageal cancer patients, respectively. The aim of this study was to externally validate both models in patients with stage III NSCLC.

Methods

Patients who underwent concurrent CRT for stage III NSCLC in 2019-2021 were studied. Outcomes were grade ≥3 and grade 4 lymphopenia during CRT. The Christie model predictors for grade ≥3 lymphopenia included age, baseline lymphocyte count, radiotherapy duration, chemotherapy, mean heart and lung doses, and thoracic vertebrae V20Gy. MDACC predictors for grade 4 lymphopenia were age, baseline lymphocyte count, planning target volume (PTV), and BMI. The external performance of both models was assessed.

Results

Among 100 patients, 78 patients (78%) developed grade ≥3 lymphopenia, with grade 4 lymphopenia in 17 (17%). For predicting grade ≥3 lymphopenia, the Christie and MDACC models yielded c-statistics of 0.77 and 0.79, respectively. For predicting grade 4 lymphopenia, c-statistics were 0.69 and 0.80, respectively. Calibration for the Christie and MDACC models demonstrated moderate and good agreement, respectively.

Conclusion

The PTV-based MDACC prediction model for severe RIL demonstrated superior external performance in NSCLC patients compared to the dosimetry-based Christie model. As such, the MDACC model can aid in identifying patients at high risk for severe lymphopenia. However, to optimize radiotherapy planning, further improvement and external validation of dosimetry-based models is desired.

Adaptive FDG-PET/CT guided dose escalation in head and neck squamous cell carcinoma: Late toxicity and oncologic outcomes (The ADMIRE study)

Purpose

To report on the late toxicity and local control (LC) of head and neck cancer patients treated with adaptive FDG-PET/CT response-guided radiotherapy (ADMIRE) with dose escalation (NCT03376386).

Materials and methods

Between December 2017 and April 2019, 20 patients with stage II-IV squamous cell carcinoma of the larynx, hypopharynx or oropharynx were treated within the ADMIRE study where FDG-PET/CT response-guided (Week 2&4) dose escalation was applied (total dose 70-78 Gy). Cisplatin or cetuximab was added to radiotherapy in case of T3-4 and/or N2c disease. To compare the LC and late toxicity of the study population, we used an external control group (n = 67) consisting of all eligible patients for the study (but not participated). These patients were treated in our institution during the same period with the current standard of 70 Gy radiotherapy. To reduce the effect of confounding, logistic regression analyses was done using stabilized inverse probability of treatment weighting (SIPTW).

Results

After median follow-up of 40 and 43 months for the ADMIRE and control groups, the 3-year LC-rates were 74% and 78%, respectively (adjusted HR after SIPTW 0.80, 95 %CI 0.25-2.52, p = 0.70). The incidences of any late G3 toxicity were 35% and 18%, respectively. The adjusted OR for any late G3 toxicity was 5.09 (95 %CI 1.64-15.8, p = 0.005), for any late G ≥ 2 toxicity was 3.67 (95 %CI 1.2-11.7, p = 0.02), for persistent laryngeal edema was 10.95 (95% CI 2.71-44.29, p = 0.001), for persistent mucosal ulcers was 4.67 (95% CI 1.23-17.7, p = 0.023), and for late G3 radionecrosis was 15.69 (95 %CI 2.43-101.39, p = 0.004).

Conclusion

Given the comparable LC rates with increased late toxicity in the ADMIRE group, selection criteria for future adaptive dose escalation trials (preferably randomized) need to be refined to include only patients at higher risk of local failure and/or lower risk of severe late toxicity.

Interobserver Variation in Tumor Delineation of Liver Metastases using Magnetic Resonance Imaging

PURPOSE/OBJECTIVE(S)

Magnetic Resonance (MR-) guided stereotactic body radiotherapy enables accurate treatment of liver metastases. Despite the accuracy of treatment delivery, one of the main uncertainties is the variation in tumor delineation, which requires additional treatment margins. The aim of this study was to quantify the interobserver variation (IOV) in MRI based delineation of the gross tumor volume (GTV) of liver metastases.

MATERIALS/METHODS

A cohort of 20 liver metastases treated on a MR-Linac were consecutively selected per primary tumor (colorectal (6), breast (6) and lung (8)). Planning MRI scans (T1 weighted with IV-contrast) were collected and anonymized. GTV delineation guidelines and case-specific information were provided to 8 radiation oncologists from 8 institutions. All cases were quantitatively reviewed and delineations with major violations of the guidelines were marked as outliers (such as contours including a vein or excluding obvious parts of a tumor). IOV was quantified by comparing individual delineations with the median contour and calculating the standard deviation (SD) and 95th percentile Hausdorff distance (HD95). Analyses were conducted on all delineations and on a subgroup excluding outliers per case to distinguish between accuracy of delineation and individual guideline interpretation. Sphericity was calculated from the volume and surface area of all delineations. Correlation between SD and sphericity was determined using Spearman's rs. Differences in SD between primary tumors were analyzed using the Kruskal-Wallis test.

RESULTS

The median volume of all metastases was 6.5 cc (IQR 3.7 - 29.8 cc) and sphericity was 0.84 (IQR 0.80 - 0.87) based on all contours (Table 1). The SD was 1.6 mm and the median HD95 was 2.7 mm. In the subgroup analysis, one (in 15 cases) or two (in 5 cases) delineations were excluded. Subgroup analyses showed a SD of 1.1 mm and median HD 95 of 2.6mm. The Kruskal-Wallis test showed no difference (p = 0.59) in SD between primary tumors. There was a significant negative correlation between IOV (SD) and sphericity (rs = -0.70; p = 0.008). Table 1: Overview of SD (mm), HD95 (mm) and volume (cc) of all observers and subgroup per primary tumor and in total CONCLUSION: MRI based GTV delineation variation of liver metastases is 1.1 - 1.6 mm, which is smaller than anticipated, although significant outliers (HD95 up to 3.9 mm) were present. Use of common delineation guidelines will ensure consistency in contouring and have the potential to decrease treatment margins when taking IOV into account.

Outcomes of Tolerability, Acute Toxicity and Quality of Life from MR-Guided Radiation Therapy (1.5T MR-Linac) for Liver Metastases in the MOMENTUM Study

PURPOSE/OBJECTIVE(S)

Stereotactic body radiation therapy (SBRT) offers an important treatment option for metastatic liver tumors. The introduction of magnetic resonance (MR) guided SBRT has paved the way for optimal tumor visualization and daily plan adaptation. The purpose of this study is to review tolerability of MR-guided SBRT of liver metastases and to present early toxicity and quality of life outcomes.

MATERIALS/METHODS

All patients with liver metastases who were treated on a 1.5T MR-Linac and enrolled in the MOMENTUM study (NTC04075305) were included. Patients were treated between April 2019 and December 2022 in 5 different institutes across 3 countries. Descriptive statistics were used to present the tolerability of treatment, toxicity (CTCAE v5.0) and quality of life outcomes (QLQ C30 and EQ 5D-5L) at baseline and 3 months after treatment.

RESULTS

A total of 127 patients with liver metastases were included in the analysis. There were 64 females and 63 men, with a median age of 66 years (range 31 to 93). The median ECOG-score was 0 (range 0-2). The most common primary origin was colorectal cancer (66%), followed by bronchus and lung cancer (12%), with ocular melanoma, pancreatic and breast cancer being joint third (all 6-7%). Fractionation schedules ranged from 12 - 67.5 Gy in 2 - 12 fractions. The most commonly prescribed fractionation dose was 60 Gy in 3-5 fractions (53% and 13% respectively) and 50 Gy in 5 fractions (11%). Completion data was available for 116 patients. 112 patients (97%) received all fractions. 4 Patients (3%) did not complete treatment due to technical issues and 2 of the 4 receiving no treatment on the MR-Linac. Physician reported toxicity at 3 months was recorded for 82 patients (66%). No grade 4 or 5 toxicities were reported. There were 12 grade 3 toxicities reported in 6 (7%) patients with 5 deemed radiation therapy related (Table 1) and 34 grade 2 toxicities in 21 (26%) patients.

CONCLUSION

We have presented the largest cohort (to our knowledge) of 127 patients treated using 1.5 Tesla MR Guidance for metastatic liver tumors. 97% of treatments were completed successfully with all treatments being well tolerated. Acute grade 3 toxicity was reported in 7% of patients with no grade 4 or 5 toxicities present. These outcomes suggest radiotherapy on the MR-Linac is a safe and promising treatment for patients with liver metastases. Additional prospective follow up is ongoing for late toxicity events and long-term control data. Table 1: Grade ≥3 toxicity at 3 months related to radiation therapy (total No. of patients was 82) There was QLQ-C30 data on 89 patients at baseline and on 62 patients at 3 months. At 3 months the median score was worse for physical functioning, VAS score and pain.

Endoscopically placed fiducial markers for image-guided radiotherapy in preoperative gastric cancer: Technical feasibility and potential benefit

Background and study aims Fiducial markers have demonstrated clinical value in radiotherapy in several organs, but little is known about markers in the stomach. Here, we assess the technical feasibility of endoscopic placement of markers in gastric cancer patients and their potential benefit for image-guided radiotherapy (IGRT). Patients and methods In this prospective feasibility study, 14 gastric cancer patients underwent endoscopy-guided gold (all patients) and liquid (7 patients) marker placements distributed throughout the stomach. Technical feasibility, procedure duration, and potential complications were evaluated. Assessed benefit for IGRT comprised marker visibility on acquired imaging (3-4 computed tomography [CT] scans and 19-25 cone-beam CTs [CBCTs] per patient) and lack of migration. Marker visibility was compared per marker type and location (gastroesophageal junction (i.e., junction/cardia), corpus (corpus/antrum/fundus), and pylorus). Results Of the 93 marker implantation attempts, 59 were successful, i.e., marker in stomach wall and present during entire 5-week radiotherapy course (2-6 successfully placed markers per patient), with no significant difference (Fisher's exact test; P >0.05) in success rate between gold (39/66=59%) and liquid (20/27=74%). Average procedure duration was 24.4 min (range 16-38). No procedure-related complications were reported. All successfully placed markers were visible on all CTs, with 81% visible on ≥95% of CBCTs. Five markers were poorly visible (on <75% of CBCTs), possibly due to small marker volume and peristaltic motion since all five were liquid markers located in the corpus. No migration was observed. Conclusions Endoscopic placement of fiducial markers in the stomach is technically feasible and safe. Being well visible and positionally stable, markers provide a potential benefit for IGRT.

Dosimetric benefit of a library of plans versus single-plan strategy for pre-operative gastric cancer radiotherapy

BACKGROUND AND PURPOSE

The stomach experiences large volume and shape changes during pre-operative gastric radiotherapy. This study evaluates the dosimetric benefit for organs-at-risk (OARs) of a library of plans (LoP) compared to the traditional single-plan (SP) strategy.

MATERIALS AND METHODS

Twelve patients who received SP CBCT-guided pre-operative gastric radiotherapy (45 Gy; 25 fractions) were included. Clinical target volume (CTV) consisted of CTV_stomach (i.e., stomach + 10 mm uniform margin minus OARs) and CTV_LN (i.e., regional lymph node stations). For LoP, five stomach volumes (approximately equidistant with fixed volumes) were created using a previously developed stomach deformation model (volume = 150-750 mL). Appropriate planning target volume (PTV) margins were calculated for CTV_stomach (SP and LoP, separately) and CTV_LN. Treatment plans were automatically generated/optimized and the best-fitting library plan was manually selected for each daily CBCT. OARs (i.e., liver, kidneys, heart, spleen, spinal canal) doses were accumulated and dose-volume histogram (DVH) parameters were evaluated.

RESULTS

The non-isotropic PTV_stomach margins were significantly (p < 0.05) smaller for LoP than SP (median = 13.1 vs 19.8 mm). For each patient, the average PTV was smaller using a LoP (difference range 134-1151 mL). For all OARs except the kidneys, DVH parameters were significantly reduced using a LoP. Differences in mean dose (Dmean) for liver, heart and spleen ranged between -1.8 to 5.7 Gy. For LoP, a benefit of heart Dmean > 4 Gy and spleen Dmean > 2 Gy was found in 4 and 5 patients, respectively.

CONCLUSION

A LoP strategy for pre-operative gastric cancer reduced average PTV and reduced OAR dose compared to a SP strategy, thereby potentially reducing risks for radiation-induced toxicities.

Dose accumulation for MR-guided adaptive radiotherapy: From practical considerations to state-of-the-art clinical implementation

MRI-linear accelerator (MR-linac) devices have been introduced into clinical practice in recent years and have enabled MR-guided adaptive radiation therapy (MRgART). However, by accounting for anatomical changes throughout radiation therapy (RT) and delivering different treatment plans at each fraction, adaptive radiation therapy (ART) highlights several challenges in terms of calculating the total delivered dose. Dose accumulation strategies-which typically involve deformable image registration between planning images, deformable dose mapping, and voxel-wise dose summation-can be employed for ART to estimate the delivered dose. In MRgART, plan adaptation on MRI instead of CT necessitates additional considerations in the dose accumulation process because MRI pixel values do not contain the quantitative information used for dose calculation. In this review, we discuss considerations for dose accumulation specific to MRgART and in relation to current MR-linac clinical workflows. We present a general dose accumulation framework for MRgART and discuss relevant quality assurance criteria. Finally, we highlight the clinical importance of dose accumulation in the ART era as well as the possible ways in which dose accumulation can transform clinical practice and improve our ability to deliver personalized RT.

Quantifying intrafractional gastric motion using auto-segmentation on MRI: Deformation and respiratory-induced displacement compared

BACKGROUND AND PURPOSE

For accurate pre-operative gastric radiotherapy, intrafractional changes must be taken into account. The aim of this study is to quantify local gastric deformations and compare these deformations with respiratory-induced displacement.

MATERIALS AND METHODS

Coronal 2D MRI scans (15-16 min; 120 repetitions of 25-27 interleaved slices) were obtained for 18 healthy volunteers. A deep-learning network was used to auto-segment the stomach. To separate out respiratory-induced displacements, auto-segmentations were rigidly shifted in superior-inferior (SI) direction to align the centre of mass (CoM) within every slice. From these shifted auto-segmentations, 3D iso-probability surfaces (isosurfaces) were established: a reference surface for P_Occ  = 0.50 and 50 other isosurfaces (from P_Occ  = 0.01 to 0.99), with P_Occ indicating the probability of occupation by the stomach. For each point on the reference surface, distances to all isosurfaces were determined and a cumulative Gaussian was fitted to this probability-distance dataset to obtain a standard deviation (SD_deform ) expressing local deformation. For each volunteer, we determined median and 98^th percentile of SD_deform over the reference surface and compared these with the respiratory-induced displacement SD_resp , that is, the SD of all CoM shifts (paired Wilcoxon signed-rank, α = 0.05).

RESULTS

Larger deformations were mostly seen in the antrum and pyloric region. Median SD_deform (range, 2.0-2.9 mm) was smaller than SD_resp (2.7-8.8 mm) for each volunteer (p < 0.00001); 98^th percentile of SD_deform (3.2-7.3 mm) did not significantly differ from SD_resp (p = 0.13).

CONCLUSION

Locally, gastric deformations can be large. Overall, however, these deformations are limited compared to respiratory-induced displacement. Therefore, unless respiratory motion is considerably reduced, the need to separately include these deformation uncertainties in the treatment margins may be limited.

Detailed dosimetric evaluation of inter-fraction and respiratory motion in lung stereotactic body radiation therapy based on daily 4D cone beam CT images

Objective. Periodic respiratory motion and inter-fraction variations are sources of geometric uncertainty in stereotactic body radiation therapy (SBRT) of pulmonary lesions. This study extensively evaluates and validates the separate and combined dosimetric effect of both factors using 4D-CT and daily 4D-cone beam CT (CBCT) dose accumulation scenarios.Approach. A first cohort of twenty early stage or metastatic disease lung cancer patients were retrospectively selected to evaluate each scenario. The planned-dose (3D_Ref) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4D_Ref), inter-fractional variations (3D_Acc) and the combined effect of both factors (4D_Acc), three dose accumulation scenarios based on 4D-CT, daily mid-cone beam CT (CBCT) position and 4D-CBCT were implemented via CT-CT/CT-CBCT deformable image registration (DIR) techniques. Each scenario was compared to 3D_Ref.A separate cohort of ten lung SBRT patients was selected to validate DIR techniques. The distance discordance metric (DDM) was implemented per voxel and per patient for tumor and organs at risk (OARs), and the dosimetric impact for CT-CBCT DIR geometric errors was calculated.Main results.Median and interquartile range (IQR) of the dose difference per voxel were 0.05/2.69 Gy and -0.12/2.68 Gy for3DAcc-3DRefand4DAcc-3DRef.For4DRef-3DRefthe IQR was considerably smaller -0.15/0.78 Gy. These findings were confirmed by dose volume histogram parameters calculated in tumor and OARs. For CT-CT/CT-CBCT DIR validation, DDM (95th percentile) was highest for heart (6.26 mm)/spinal cord (8.00 mm), and below 3 mm for tumor and the rest of OARs. The dosimetric impact of CT-CBCT DIR errors was below 2 Gy for tumor and OARs.Significance. The dosimetric impact of inter-fraction variations were shown to dominate those of periodic respiration in SBRT for pulmonary lesions. Therefore, treatment evaluation and dose-effect studies would benefit more from dose accumulation focusing on day-to-day changes then those that focus on respiratory motion.

Adaptive margins for online adaptive radiotherapy

Objective. In online adaptive radiotherapy a new plan is generated every fraction based on the organ and clinical target volume (CTV) delineations of that fraction. This allows for a planning target volume margin that does not need to be constant over the whole course of treatment, as is the case in conventional radiotherapy. This work aims to introduce an approach to update the margins each fraction based on the per-patient treatment history and explore the potential benefits of such adaptive margins. Approach. We introduce a novel methodology to implement adaptive margins, isotropic and anisotropic, during a treatment course based on the accumulated dose to the CTV. We then simulate treatment histories for treatments delivered in up to 20 fractions using various choices for the standard deviations of the systematic and random errors and homogeneous and inhomogeneous dose distributions. The treatment-averaged adaptive margin was compared to standard constant margins. The change in the minimum dose delivered to the CTV was compared on a patient and a population level. All simulations were performed within the van Herk approach and its known limitations. Main results. The population mean treatment-averaged margins are down to 70% and 55% of the corresponding necessary constant margins for the isotropic and anisotropic approach. The reduction increases with longer fractionation schemes and an inhomogeneous target dose distribution. Most of the benefit can be attributed to the elimination of the effective systematic error over the course of treatment. Interpatient differences in treatment-averaged margins were largest for the isotropic margins. For the 10% of patients that would receive a lower than prescribed dose to the CTV this minimum dose to the CTV is increased using the adaptive margin approaches. Significance. Adaptive margins can allow to reduce margins in most patients without compromising patients with greater than average target motion.

Bronchial stenosis in central pulmonary tumors treated with Stereotactic Body Radiation Therapy: Bronchial stenosis in central lung tumors after SBRT

PURPOSE

Stereotactic body radiotherapy (SBRT) in lung tumors has an excellent local control due to the high delivered dose. Proximity of the proximal bronchial tree (PBT) to the high dose area may result in pulmonary toxicity. Bronchial stenosis is an adverse event that can occur after high dose to the PBT. Literature on the risk of developing bronchial stenosis is limited. We therefore evaluated the risk of bronchial stenosis for tumors central to the PBT and correlated the dose to the bronchi.

METHODS AND MATERIALS

Patients with a planning tumor volume (PTV) ≤2cm from PBT receiving SBRT (8 × 7.5Gy) between 2015-2019 were retrospectively reviewed. Main bronchi and lobar bronchi were manually delineated. Follow-up CT-scans were analyzed for bronchial stenosis and atelectasis. Bronchial stenosis was assessed using CTCAEv4. Patient, tumor, dosimetric factors and survival were evaluated between patients with and without stenosis using uni- and multivariate and Kaplan Meier analysis.

RESULTS

Fifty-one patients were analyzed with a median age of 70 years and WHO≤1 in 92.2%. Median follow-up was 36 months (IQR 19.6-45.4) and median OS 48 months (IQR 21.5-59.3). In fifteen patients (29.4%) bronchial stenosis was observed on FU-CT-scan. Grade 1 stenosis was seen in 21.6% (n=11), grade 2 in 7.8% (n=4). No grade ≥3 stenosis was observed. Median time to stenosis was 9.6 months (IQR 4.4-19.2). Patients who developed stenosis had significantly larger gross tumor volume (GTV) with a median of 19cc (IQR 7.7-63.2) versus 5.2cc (IQR 1.7-11.3, p<0.01). Prognostic factors in multivariate analysis for stenosis were age (p=0.03; OR 1.1), baseline dyspnea (p=0.02 OR 7.7) and the mean lobar bronchus dose (p=0.01; OR 1.1).

CONCLUSION

Low grade (≤2) lobar bronchial stenosis is a complication in approximately one third of patients following SBRT for lung tumors with a PTV ≤2cm from PBT. Prognostic risk factors were age, baseline dyspnea and mean dose on a lobar bronchus.

Reducing 4DCBCT imaging dose and time: exploring the limits of adaptive acquisition and motion compensated reconstruction

This study investigates the dose and time limits of adaptive 4DCBCT acquisitions (adaptive-acquisition) compared with current conventional 4DCBCT acquisition (conventional-acquisition). We investigate adaptive-acquisitions as low as 60 projections (∼25 s scan, 6 projections per respiratory phase) in conjunction with emerging image reconstruction methods. 4DCBCT images from 20 patients recruited into the adaptive CT acquisition for personalized thoracic imaging clinical study (NCT04070586) were resampled to simulate faster and lower imaging dose acquisitions. All acquisitions were reconstructed using Feldkamp–Davis–Kress (FDK), McKinnon–Bates (MKB), motion compensated FDK (MCFDK), motion compensated MKB (MCMKB) and simultaneous motion estimation and image reconstruction (SMEIR) algorithms. All reconstructions were compared against conventional-acquisition 4DFDK-reconstruction using Structural SIMilarity Index (SSIM), signal-to-noise ratio (SNR), contrast-to-noise-ratio (CNR), tissue interface sharpness diaphragm (TIS-D), tissue interface sharpness tumor (TIS-T) and center of mass trajectory (COMT) for difference in diaphragm and tumor motion. All reconstruction methods using 110-projection adaptive-acquisition (11 projections per respiratory phase) had a SSIM of greater than 0.92 relative to conventional-acquisition 4DFDK-reconstruction. Relative to conventional-acquisition 4DFDK-reconstruction, 110-projection adaptive-acquisition MCFDK-reconstructions images had 60% higher SNR, 10% higher CNR, 30% higher TIS-T and 45% higher TIS-D on average. The 110-projection adaptive-acquisition SMEIR-reconstruction images had 123% higher SNR, 90% higher CNR, 96% higher TIS-T and 60% higher TIS-D on average. The difference in diaphragm and tumor motion compared to conventional-acquisition 4DFDK-reconstruction was within submillimeter accuracy for all acquisition reconstruction methods. Adaptive-acquisitions resulted in faster scans with lower imaging dose and equivalent or improved image quality compared to conventional-acquisition. Adaptive-acquisition with motion compensated-reconstruction enabled scans with as low as 110 projections to deliver acceptable image quality. This translates into 92% lower imaging dose and 80% less scan time than conventional-acquisition.

The dosimetric and clinical advantages of the GTV-CTV-PTV margins reduction by 6 mm in head and neck squamous cell carcinoma: Significant acute and late toxicity reduction

PURPOSE

We aim to identify the dosimetric and clinical impact of reducing the total GTV-CTV-PTV margins in head-and-neck squamous cell carcinoma (HNSCC) treated with definitive (chemo)radiation.

MATERIALS AND METHODS

The acute and late toxicity and outcomes of 155 consecutive patients treated between February 2017 and March 2019 with GTV-CTV-PTV margins of 9 mm were compared to those of 155 consecutive patients treated with total margin of 15 mm margin, before April 2015. All patients were treated with VMAT with daily-image guidance using CBCT.

RESULTS

Reducing the GTV-CTV-PTV by 6 mm resulted in significant reduction of total irradiated volume (PTV-total) by a median of 28.1% and significant reduction of doses to all salivary glands (largest reduction ipsilateral parotid gland; median -9.6 Gy) and constrictor muscle (-6.1 Gy) with subsequent reduction of the incidence of overall acute grade 3 toxicity (47.7% for 9 mm and 66.5% for 15 mm groups, p = 0.001), grade 3 mucositis (18.1% vs. 35.5%, p < 0.001) and feeding tube-dependency at the end of treatment (24.5% vs. 40%, p = 0.005). The incidence of late grade ≥ 2 xerostomia and dysphagia were also significantly lower in the 9 mm group (31.7% vs. 58.6% p < 0.001, and 15.4% vs. 26.7%, p = 0.04). The 2-year rates of loco-regional control, disease-free and overall survival were 78.8% vs.75.8%, 70.9% vs. 64.4%, and 83.8% vs. 67.6%, (p > 0.05, all).

CONCLUSION

Reduction of the total GTV-CTV-PTV margins from 15 to 9 mm in HNSCC significantly reduced the irradiated volumes and the dose to salivary glands and constrictor muscle with significant reduction of radiation-related toxicity. The loco-regional control rates of both groups were comparable.

Image quality of dual-energy cone-beam CT with total nuclear variation regularization

Despite the improvements in image quality of cone beam computed tomography (CBCT) scans, application remains limited to patient positioning. In this study, we propose to improve image quality by dual energy (DE) imaging and iterative reconstruction using least squares fitting with total variation (TV) regularization. The generalization of TV called total nuclear variation (TNV) was used to generate DE images. We acquired single energy (SE) and DE scans of an image quality phantom (IQP) and of an anthropomorphic human male phantom (HMP). The DE scans were dual arc acquisitions of 70kV and 130kV with a variable dose partitioning between low energy (LE) and high energy (HE) arcs. To investigate potential benefits from a larger spectral separation between LE and HE, DE scans with an additional 2 mm copper beam filtration in the HE arc were acquired for the IQP. The DE TNV scans were compared to SE scans reconstructed with FDK and iterative TV with varying parameters. The contrast-to-noise ratio (CNR), spatial frequency, and structural similarity (SSIM) were used as image quality metrics. Results showed largely improved image quality for DE TNV over FDK for both phantoms. DE TNV with the highest dose allocation in the LE arm yielded the highest CNR. Compared to SE TV, these DE TNV results had a slightly lower CNR with similar spatial resolution for the IQP. A decrease in the dose allocated to the LE arm improved the spatial resolution with a trade-off against CNR. For the HMP, DE TNV displayed a lower CNR and/or lower spatial resolution depending on the reconstruction parameters. Regarding the SSIM, DE TNV was superior to FDK and SE TV for both phantoms. The additional beam filtration for the IQP led to improved image quality in all metrics, surpassing the SE TV results in CNR and spatial resolution.

Improving predictive CTV segmentation on CT and CBCT for cervical cancer by diffeomorphic registration of a prior

PURPOSE

Automatic cervix-uterus segmentation of the clinical target volume (CTV) on CT and cone beam CT (CBCT) scans is challenged by the limited visibility and the non-anatomical definition of certain border regions. We study potential performance gain of convolutional neural networks by regulating the segmentation predictions as diffeomorphic deformations of a segmentation prior.

METHODS

We introduce a 3D convolutional neural network (CNN) which segments the target scan by joint voxel-wise classification and the registration of a given prior. We compare this network to two other 3D baseline models: one treating segmentation as a classification problem (segmentation-only), the other as a registration problem (deformation-only). For reference and to highlight benefits of a 3D model, these models are also benchmarked against a 2D segmentation model. Network performances are reported for CT and CBCT segmentation of the cervix-uterus CTV. We train the networks on data of 84 patients. The prior is provided by the CTV segmentation of a planning CT. Repeat CT or CBCT scans constitute the target scans to be segmented.

RESULTS

All 3D models outperformed the 2D segmentation model. For CT segmentation, combining classification and registration in the proposed joint model proved beneficial, achieving a Dice score of 0.87 and a mean squared error (MSE) of the surface distance below 1.7 mm. No such synergy was observed for CBCT segmentation, for which the joint and the deformation-only model performed similarly, achieving a Dice score of about 0.80 and a MSE surface distance of 2.5 mm. However, the segmentation-only model performed notably worse in this low contrast regime. Visual inspection revealed that this performance drop translated into geometric inconsistencies between the prior and target segmentation. Such inconsistencies where not observed for the deformation-based models.

CONCLUSION

Constraining the solution space of admissible segmentation predictions to those reachable by a diffeomorphic deformation of the prior proved beneficial as it improved geometric consistency. Especially for CBCT, with its poor soft tissue contrast, this type of regularization becomes important as shown by quantitative and qualitative evaluation. This article is protected by copyright. All rights reserved.

Quantifying the sensitivity of target dose on intra-fraction displacement in intra-cranial stereotactic radiosurgery

BACKGROUND/PURPOSE

Mask-immobilized stereotactic radiosurgery (SRS) using a gating window is an emerging technology. However, the amount of intracranial tumor motion that can be tolerated during treatment while satisfying clinical dosimetric goals is unknown. The purpose of this study was to quantify the sensitivity of target dose to tumor motion.

METHODS

In clinical SRS plans, where a nose marker was tracked as surrogate for target motion, translational and rotational target movements were simulated using nose-marker displacements of ±0.5mm, ±1.0mm or ±1.5mm. The effect on minimum dose to 99% of the target (D₉₉) and percent target coverage by prescription dose was quantified using mixed-effect modelling with variables: displacement, target volume and location.

RESULTS

The effect on dose metrics is statistically larger for translational displacements compared to rotational displacements, and the effect of pitch rotations is statistically larger compared to yaw rotations. The mixed-effect model for translations showed that displacement and target volume are statistically significant variables, for rotation the variable target distance to rotation axis is additionally significant. For mean target volume (12.6cc) and translational nose-marker displacements of 0.5mm, 1.0mm, and 1.5mm, D₉₉ decreased by 2.2%, 7.1% and 13.0%, and coverage by 0.4%, 1.8% and 4.4%, respectively. For mean target volume, mean distance midpoint-target to pitch axis (7.6cm), and rotational nose-marker displacement of 0.5mm, 1.0mm, and 1.5mm, D₉₉ decreased by 1.0%, 3.6% and 6.9%, and coverage by 0.2%, 0.8% and 1.9%, respectively. For rotational yaw axis displacement, mean distance midpoint-target axis (4.2cm), D₉₉ decreased by 0.3%, 1.2% and 2.5%, and coverage by 0.1%, 0.2% and 0.5%, respectively.

CONCLUSION

Simulated target displacements showed that sensitivity of tumor dose to motion depends on both target volume and target location. Suggesting that patient- and target-specific thresholds may be implemented for optimizing the balance between dosimetric plan accuracy and treatment prolongation caused by out-of-tolerance motion.

Master protocol trial design for technical feasibility of MR-guided radiotherapy

The master protocol trial design aims to increase efficiency in terms of trial infrastructure and protocol administration which may accelerate development of (technical) innovations in radiation oncology. A master protocol to study feasibility of techniques/software for MR-guided adaptive radiotherapy with the MR-Linac is described and discussed.

Deformation trajectory prediction using a neural network trained on finite element data-application to library of CTVs creation for cervical cancer

Purpose. We propose a neural network for fast prediction of realistic, time-parametrized deformations between pairs of input segmentations. The proposed method was used to generate a library of planning CTVs for cervical cancer radiotherapy.Methods.A 3D convolutional neural network (CNN) was introduced to predict a stationary velocity field given the distance maps of the cervix CTV in empty and full bladder anatomy. Diffeomorphic deformation trajectories between the two states were obtained by time integration. Intermediate deformation states were used to populate a library of cervix CTVs. The network was trained on cervix CTV deformations of 20 patients generated by finite element modeling (FEM). Validation was performed on FEM data of 9 healthy volunteers. Additionally, for these subjects, CTV deformations were observed in a series of repeat MR scans as the bladder filled from empty to full. Predicted and FEM libraries were compared, and benchmarked against the observed deformations. Finally, for an independent test set of 20 patients the predicted libraries were evaluated clinically, and compared to the current method.Results.The median Dice score over the validation subjects between the predicted and FEM libraries was >0.95 throughout the deformation, with a median 90 percentile surface distance of <3 mm. The ability to cover observed CTVs was similar for both the FEM-based and the proposed method, with residual offsets being about twice as large as the difference between the two methods. Clinical evaluation showed improved library properties over the method currently used in clinic.Conclusions.We proposed a CNN trained on FEM deformations, which predicts the deformation trajectory between two input states of the cervix CTV in one forward pass. We applied this to CTV library prediction for cervical cancer. The network is able to mimic FEM deformations well, while being much faster and simpler in use.

MR-Linac Radiotherapy - The Beam Angle Selection Problem

Background

With the large-scale introduction of volumetric modulated arc therapy (VMAT), selection of optimal beam angles for coplanar static-beam IMRT has increasingly become obsolete. Due to unavailability of VMAT in current MR-linacs, the problem has re-gained importance. An application for automated IMRT treatment planning with integrated, patient-specific computer-optimization of beam angles (BAO) was used to systematically investigate computer-aided generation of beam angle class solutions (CS) for replacement of computationally expensive patient-specific BAO. Rectal cancer was used as a model case.

Materials and Methods

23 patients treated at a Unity MR-linac were included. BAO_x plans (x=7-12 beams) were generated for all patients. Analyses of BAO₁₂ plans resulted in CS_x class solutions. BAO_x plans, CS_x plans, and plans with equi-angular setups (EQUI_x, x=9-56) were mutually compared.

Results

For x>7, plan quality for CS_x and BAO_x was highly similar, while both were superior to EQUI_x. E.g. with CS₉, bowel/bladder D_mean reduced by 22% [11%, 38%] compared to EQUI₉ (p<0.001). For equal plan quality, the number of EQUI beams had to be doubled compared to BAO and CS.

Conclusions

Computer-generated beam angle CS could replace individualized BAO without loss in plan quality, while reducing planning complexity and calculation times, and resulting in a simpler clinical workflow. CS and BAO largely outperformed equi-angular treatment. With the developed CS, time consuming beam angle re-optimization in daily adaptive MR-linac treatment could be avoided. Further systematic research on computerized development of beam angle class solutions for MR-linac treatment planning is warranted.

Comparisons of normal tissue complication probability models derived from planned and delivered dose for head and neck cancer patients

BACKGROUND AND PURPOSE

Normal tissue complication probability (NTCP) models are typically derived from the planned dose distribution, which can deviate from the delivered dose due to anatomical day-to-day variations. The aim of this study was to compare NTCP models derived from the planned and the delivered dose for head and neck cancer (HNC) patients.

MATERIAL AND METHOD

322 HNC patients who received radiotherapy with daily CBCT guidance were included in this retrospective study. The delivered dose was estimated by deformably accumulating dose from daily CBCT to planning anatomy. We used a Lyman-Kutcher-Burman NTCP model, to relate the equivalent uniform dose (EUD) of organs at risk (OAR) with oral mucositis, xerostomia and dysphagia respectively. We compared the model parameters and performances.

RESULTS

The median differences between planned and delivered EUD to the OARs were significantly larger for patients with toxicity than without for acute dysphagia (≥G2 and ≥G3) and late dysphagia (≥G3) (p < 0.05). Those differences resulted in small differences in steepness and agreement to the data between delivered- and planned-fitted NTCP curves, and the differences were not significant. The differences in AUC were less than 0.01.

CONCLUSION

Differences between delivered and planned dose did not lead to significant differences in NTCP curves. The additional clinical relevance of NTCP models using accumulated dose for oral mucositis, xerostomia and dysphagia in HNC radiotherapy is likely to be limited.

Deterioration of Intended Target Volume Radiation Dose Due to Anatomical Changes in Patients with Head-and-Neck Cancer

Delivered radiation dose can differ from intended dose. This study quantifies dose deterioration in targets, identifies predictive factors, and compares dosimetric to clinical patient selection for adaptive radiotherapy in head-and-neck cancer patients. One hundred and eighty-eight consecutive head-and-neck cancer patients treated up to 70 Gy were analyzed. Daily delivered dose was calculated, accumulated, and compared to the planned dose. Cutoff values (1 Gy/2 Gy) were used to assess plan deterioration in the highest/lowest dose percentile (D₁/D₉₉). Differences in clinical factors between patients with/without dosimetric deterioration were statistically tested. Dosimetric deterioration was evaluated in clinically selected patients for adaptive radiotherapy with CBCT. Respectively, 16% and 4% of patients had deterioration over 1 Gy in D₉₉ and D₁ in any of the targets, this was 5% (D₉₉) and 2% (D₁) over 2 Gy. Factors associated with deterioration of D₉₉ were higher baseline weight/BMI, weight gain early in treatment, and smaller PTV margins. The sensitivity of visual patient selection with CBCT was 22% for detection of dosimetric changes over 1 Gy. Large dose deteriorations in targets occur in a minority of patients. Clinical prediction based on patient characteristics or CBCT is challenging and dosimetric selection tools seem warranted to identify patients in need for ART, especially in treatment with small PTV margins.

Reduction of GTV to high-risk CTV radiation margin in head and neck squamous cell carcinoma significantly reduced acute and late radiation-related toxicity with comparable outcomes

BACKGROUND AND PURPOSE

We aim to retrospectively investigate whether reducing GTV to high-risk CTV margin will significantly reduce acute and late toxicity without jeopardizing outcome in head-and-neck squamous cell carcinoma (HNSCC) treated with definitive (chemo)radiation.

MATERIALS AND METHODS

Between April 2015 and April 2019, 155 consecutive patients were treated with GTV to high-risk CTV margin of 10 mm and subsequently another 155 patients with 6 mm margin. The CTV-PTV margin was 3 mm for both groups. All patients were treated with volumetric-modulated arc therapy with daily image-guidance using cone-beam CT. End points of the study were acute and late toxicity and oncologic outcomes.

RESULTS

Overall acute grade 3 toxicity was significantly lower in 6 mm, compared to 10 mm group (48% vs. 67%, respectively, p < 0.01). The same was true for acute grade 3 mucositis (18% vs. 34%, p < 0.01) and grade ≥ 2 dysphagia (67% vs. 85%, p < 0.01). Also feeding tube-dependency at the end of treatment (25% vs. 37%, p = 0.02), at 3 months (12% and 25%, p < 0.01), and at 6 months (6% and 15%, p = 0.01) was significantly less in 6 mm group. The incidence of late grade 2 xerostomia was also significantly lower in the 6 mm group (32% vs. 50%, p < 0.01). The 2-year rates of loco-regional control, disease-free and overall survival were 78.7% vs. 73.1%, 70.6% vs. 61.4%, and 83.2% vs. 74.4% (p > 0.05, all).

CONCLUSION

The first study reporting on reduction of GTV to high-risk CTV margin from 10 to 6 mm showed significant reduction of the incidence and severity of radiation-related toxicity without reducing local-regional control and survival.

Delivered dose-effect analysis of radiation induced rib fractures after thoracic SBRT

BACKGROUND AND PURPOSE

Anatomical changes during the stereotactic body radiation therapy (SBRT) of early stage non-small cell lung cancer (NSCLC) may cause the delivered dose to deviate from the planned dose. We investigate if normal tissue complication probability (NTCP) models based on the delivered dose predict radiation-induced rib fractures better than models based on the planned dose.

MATERIAL AND METHODS

437 NSCLC patients treated to a median dose of 3x18 Gy were included. Delivered dose was estimated by accumulating EQD2-corrected fraction doses after being deformed with daily CBCT-to-planning CT deformable image registration. Dosimetric parameters D_x (dose to a relative volume x) were extracted for each rib included in the CBCTs field-of-view. An NTCP model was constructed for both planned and delivered dose, optimizing the parameters TD₅₀ (dose with 50% toxicity risk), m (steepness of the curve) and x, using maximum likelihood estimation. Best NTCP model was determined using Akaike weights (Aw). Differences between the models were tested for significance using the Vuong's test.

RESULTS

Median time to fracture of 110 fractured ribs was 22.5 months. The maximum rib dose, D₀, best predicted fractures for both planned and delivered dose. The average delivered D₀ was significantly lower than planned (p < 0.001). NTCP model based on the delivered D₀ was the best, with Aw = 0.95. The models were not significantly different.

CONCLUSION

Delivered maximum dose to the ribs was significantly lower than planned. The NTCP model based on delivered dose improved predictions of radiation-induced rib fractures but did not reach statistical significance.

The first-in-human implementation of adaptive 4D cone beam CT for lung cancer radiotherapy: 4DCBCT in less time with less dose

BACKGROUND AND PURPOSE

We present the first implementation of Adaptive 4D cone beam CT (4DCBCT) that adapts the image hardware (gantry rotation speed and kV projections) in response to the patient's real-time respiratory signal. Adaptive 4DCBCT was applied on lung cancer patients to reduce the scan time and imaging dose in the ADaptive CT Acquisition for Personalised Thoracic imaging (ADAPT) trial.

MATERIALS AND METHODS

The ADAPT technology measures the patient's real-time respiratory signal and uses mathematical optimisation and external circuitry attached to the linear accelerator to modulate the gantry rotation speed and kV projection rate to reduce scan times and imaging dose. For each patient, ADAPT scans were acquired on two treatment fractions and reconstructed with a motion compensated reconstruction algorithm and compared to the current state-of-the-art four-minute 4DCBCT acquisition (conventional 4DCBCT). We report on the scan time, imaging dose and image quality for the first four adaptive 4DCBCT patients.

RESULTS

The ADAPT imaging dose was reduced by 85% and scan times were 73 ± 12 s representing a 70% reduction compared to the 240 s conventional 4DCBCT scan. The contrast-to-noise ratio was improved from 9.2 ± 3.9 with conventional 4DCBCT to 11.7 ± 4.1 with ADAPT.

DISCUSSION

The ADAPT trial represents the first time that gantry rotation speed and projection acquisition have been adapted and optimised in real-time in response to changes in the patient's breathing. ADAPT demonstrates substantially reduced scan times and imaging dose for clinical 4DCBCT imaging that could enable more efficient and optimised lung cancer radiotherapy.

Validation of a 4D-MRI guided liver stereotactic body radiation therapy strategy for implementation on the MR-linac

Purpose. Accurate tumor localization for image-guided liver stereotactic body radiation therapy (SBRT) is challenging due to respiratory motion and poor tumor visibility on conventional x-ray based images. Novel integrated MRI and radiotherapy systems enable direct in-room tumor visualization, potentially increasing treatment accuracy. As these systems currently do not provide a 4D image-guided radiotherapy strategy, we developed a 4D-MRI guided liver SBRT workflow and validated all steps for implementation on the Unity MR-linac.Materials and Methods. The proposed workflow consists of five steps: (1) acquisition of a daily 4D-MRI scan, (2) 4D-MRI to mid-position planning-CT rigid tumor registration, (3) calculation of daily tumor midP misalignment, (4) plan adaptation using adapt-to-position (ATP) with segment-weights optimization and (5) adapted plan delivery. The workflow was first validated in a motion phantom, performing regular motion at different baselines (±5 to ±10 mm) and patient-derived respiratory signals with varying degrees of irregularity. 4D-MRI derived respiratory signals and 4D-MRI to planning CT registrations were compared to the phantom input, and gamma and dose-area-histogram analyses were performed on the delivered dose distributions on film. Additionally, 4D-MRI to CT registration performance was evaluated in patient images using the full-circle method (transitivity analysis). Plan adaption was further analyzedin-silicoby creating adapted treatment plans for 15 patients with oligometastatic liver disease.Results. Phantom trajectories could be reliably extracted from 4D-MRI scans and 4D-MRI to CT registration showed submillimeter accuracy. The DAH-analysis demonstrated excellent coverage of the dose evaluation structures GTV and GTV_TD. The median daily rigid 4D-MRI to midP-CT registration precision in patient images was <2 mm. The ATP strategy restored the target dose without increased exposure to the OARs and plan quality was independent from 3D shift distance in the range of 1-26 mm.Conclusions. The proposed 4D-MRI guided strategy showed excellent performance in all workflow tests in preparation of the clinical introduction on the Unity MR-linac.

Non-small cell lung cancer stage migration as a function of wait times from diagnostic imaging: A pooled analysis from five international centres

INTRODUCTION

Patients with non-small cell lung cancer (NSCLC) can experience rapid disease progression between initial staging FDG-PET scans and commencement of curative-intent radiotherapy (RT). Previous studies that estimated stage migration rates by comparing staging PET/CT and treatment-planning PET/CT images were limited by small sample sizes.

METHODS

This multicenter, international study combined prospective data from five institutions for PET-staged patients with NSCLC who were intended to receive curative-intent RT. TNM status was compared for staging and RT planning scans and the probability of TNM status and overall stage migration was analyzed as a function of the interval between PET/CT scans. The impacts of N classification, overall stage, and pathology were also studied.

RESULTS

Pooled data from 181 patients were analyzed. The median interval between PET/CT scans was 42 days (range, 2-208). Upstaging occurred in 32 % of patients. The overall rate of stage migration was higher for patients presenting with initial stage IIIB/IIIC disease (p = 0.006) and patients with N2-3 nodal disease (p = 0.019). Upstaging to M1 disease was significantly associated with initial stage IIIB/IIIC disease (HR = 15.2) and adenocarcinoma (HR = 10) histology.

CONCLUSION

Longer intervals between imaging and treatment in patients with NSCLC were associated with high rates disease progression with consequent risks of geographic miss in RT planning and futile treatment in patients with M1 disease. Patients with more extensive initial nodal involvement and those with adenocarcinoma had the highest rates of stage migration. Dedicated RT planning PET/CT imaging is recommended, especially if >3 weeks have elapsed after initial staging.

Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction

Conventional 4DCBCT captures 1320 projections across 4 min. Adaptive 4DCBCT has been developed to reduce imaging dose and scan time. This study investigated reconstruction algorithms that best complement adaptive 4DCBCT acquisition for reducing imaging dose and scan time whilst maintaining or improving image quality compared to conventional 4DCBCT acquisition using real patient data from the first 10 adaptive 4DCBCT patients. Adaptive 4DCBCT was implemented in the ADaptive CT Acquisition for Personalized Thoracic imaging clinical trial. Adaptive 4DCBCT modulates gantry rotation speed and kV acquisition rate in response to the patient's real-time respiratory signal, ensuring even angular spacing between projections at each respiratory phase. We examined the first 10 lung cancer radiotherapy patients that received adaptive 4DCBCT. Fast, 200-projection scans over 60-80 s, and slower, 600-projection scans over ∼240 s, were obtained after routine patient treatment and compared against conventional 4DCBCT acquisition. Adaptive 4DCBCT acquisitions were reconstructed using Feldkamp-Davis-Kress (FDK), McKinnon-Bates (MKB), Motion Compensated FDK (MCFDK) and Motion Compensated MKB (MCMKB) algorithms. Reconstructions were assessed via, Structural SIMilarity (SSIM), Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), Tissue Interface Sharpness of Diaphragm (TIS-D) and Tumor (TIS-T). The 200- and 600-projection adaptive 4DCBCT acquisition corresponded to 85% and 55% reduction in imaging dose, shorter and similar scan times of approximately 90 s and 236 s respectively, compared to conventional 4DCBCT acquisition. 200- and 600-projection adaptive 4DCBCT reconstructions achieved more than 0.900 SSIM relative to conventional 4DCBCT acquisition. Compared to conventional 4DCBCT acquisition, 200-projection adaptive 4DCBCT reconstructions achieved higher SNR, CNR, TIS-T, TIS-D with motion compensated algorithms, MCFDK (208%, 159%, 174%, 247%) and MCMKB (214%, 173%, 266%, 245%) respectively. The 200-projection adaptive 4DCBCT MCFDK- and MCMKB-reconstruction results show image quality improvements are possible even with 85% fewer projections acquired. We established acquisition-reconstruction protocols that provide substantial reductions in imaging time and dose whilst improving image quality.

Improving linac integrated cone beam computed tomography image quality using tube current modulation

PURPOSE

Linac integrated cone beam CT (CBCT) scanners have become widespread tool for image guidance in radiotherapy. The current implementation uses constant imaging fluence across all the projection angles, which leads to anisotropic noise properties and suboptimal image quality for noncircular symmetric objects. Tube current modulation (TCM) is widely used in conventional CT. The purpose of this work was to implement TCM on a linac integrated CBCT scanner and evaluate its impact on image quality under varying scatter conditions and scatter correction strategies.

METHODS

We have implemented TCM on a nonclinical Elekta Versa HD linear accelerator with enhanced x-ray generator functionality including pulse width modulation. The pulse width was modulated using two Arduino programmable microcontrollers: one placed on the kV arm to measure the projection angle and the other connected to the kV generator control board to vary x-ray pulse width as function of gantry angle and precalculated transmission. An in-house developed phantom with a ratio of the left-right to anterior-posterior path length of 1.85:1 was scanned. Image quality was determined using the anisotropicity of the 2D noise power spectra (NPS) in the transverse plane and the contrast-to-noise ratio (CNR). In addition, to determine the impact of scatter on the applicability of the TCM method we have modified the generated scatter using three different collimators in the cranio-caudal direction as well as with and without an antiscatter grid (ASG).

RESULTS

Application of the TCM led to 30-78% reduction of the angular anisotropicity of the NPS in the transverse plane. The amount of reduction depended on the scatter conditions, with lower values corresponding to higher scatter conditions. The same was true for the CNR: when scatter contribution was low (presence of an ASG or very aggressive collimation) the CNR was improved by about 30%, while in high scatter conditions the CNR was improved by about 12%.

CONCLUSIONS

TCM has the potential to improve CBCT image quality, but this depends on the amount of detected x-ray scatter.

A multivariable study of deformable image registration evaluation metrics in 4DCT of thoracic cancer patients

Deformable image registration (DIR) accuracy is often validated using manually identified landmarks or known deformations generated using digital or physical phantoms. In daily practice, the application of these approaches is limited since they are time-consuming or require additional equipment. An alternative is the use of metrics automatically derived from the registrations, but their interpretation is not straightforward. In this work we aim to determine the suitability of DIR-derived metrics to validate the accuracy of 4 commonly used DIR algorithms. First, we investigated the DIR accuracy using a landmark-based metric (target registration error (TRE)) and a digital phantom-based metric (known deformation recovery error (KDE)). 4DCT scans of 16 thoracic cancer patients along with corresponding pairwise anatomical landmarks (AL) locations were collected from two public databases. Digital phantoms with known deformations were generated by each DIR algorithm to test all other algorithms and compute KDE. TRE and KDE were evaluated at AL. KDE was additionally quantified in coordinates randomly sampled (RS) inside the lungs. Second, we investigated the associations of 5 DIR-derived metrics (distance discordance metric (DDM), inverse consistency error (ICE), transitivity (TE), spatial (SS) and temporal smoothness (TS)) with DIR accuracy through uni- and multivariable linear regression models. TRE values were found higher compared to KDE values and these varied depending on the phantom used. The algorithm with the best accuracy achieved average values of TRE = 1.1 mm and KDE ranging from 0.3 to 0.8 mm. DDM was the best predictor of DIR accuracy, with moderate correlations (R ² < 0.61). Poor correlations were obtained at AL for algorithms with better accuracy, which improved when evaluated at RS. Only slight correlation improvement was obtained with a multivariable analysis (R ² < 0.64). DDM can be a useful metric to identify inaccuracies for different DIR algorithms without employing landmarks or digital phantoms.

First system for fully-automated multi-criterial treatment planning for a high-magnetic field MR-Linac applied to rectal cancer

Background and purpose: In this study we developed a workflow for fully-automated generation of deliverable IMRT plans for a 1.5 T MR-Linac (MRL) based on contoured CT scans, and we evaluated automated MRL planning for rectal cancer.Methods: The Monte Carlo dose calculation engine used in the clinical MRL TPS (Monaco, Elekta AB, Stockholm, Sweden), suited for high accuracy dose calculations in a 1.5 T magnetic field, was coupled to our in-house developed Erasmus-iCycle optimizer. Clinically deliverable plans for 23 rectal cancer patients were automatically generated in a two-step process, i.e., multi-criterial fluence map optimization with Erasmus-iCycle followed by a conversion into a deliverable IMRT plan in the clinical TPS. Automatically generated plans (AUTOplans) were compared to plans that were manually generated with the clinical TPS (MANplans).Results: With AUTOplanning large reductions in planning time and workload were obtained; 4-6 h mainly hands-on planning for MANplans vs ∼1 h of mainly computer computation time for AUTOplans. For equal target coverage, the bladder and bowel bag D_mean was reduced in the AUTOplans by 1.3 Gy (6.9%) on average with a maximum reduction of 4.5 Gy (23.8%). Dosimetric measurements at the MRL demonstrated clinically acceptable delivery accuracy for the AUTOplans.Conclusions: A system for fully automated multi-criterial planning for a 1.5 T MR-Linac was developed and tested for rectal cancer patients. Automated planning resulted in major reductions in planning workload and time, while plan quality improved. Negative impact of the high magnetic field on the dose distributions could be avoided.

Correlating Dose Variables with Local Tumor Control in Stereotactic Body Radiation Therapy for Early-Stage Non-Small Cell Lung Cancer: A Modeling Study on 1500 Individual Treatments

BACKGROUND

Large variation regarding prescription and dose inhomogeneity exists in stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer. The aim of this modeling study was to identify which dose metric correlates best with local tumor control probability to make recommendations regarding SBRT prescription.

METHODS AND MATERIALS

We combined 2 retrospective databases of patients with non-small cell lung cancer, yielding 1500 SBRT treatments for analysis. Three dose parameters were converted to biologically effective doses (BEDs): (1) the (near-minimum) dose prescribed to the planning target volume (PTV) periphery (yielding BEDmin); (2) the (near-maximum) dose absorbed by 1% of the PTV (yielding BEDmax); and (3) the average between near-minimum and near-maximum doses (yielding BEDave). These BED parameters were then correlated to the risk of local recurrence through Cox regression. Furthermore, BED-based prediction of local recurrence was attempted by logistic regression and fast and frugal trees. Models were compared using the Akaike information criterion.

RESULTS

There were 1500 treatments in 1434 patients; 117 tumors recurred locally. Actuarial local control rates at 12 and 36 months were 96.8% (95% confidence interval, 95.8%-97.8%) and 89.0% (87.0%-91.1%), respectively. In univariable Cox regression, BEDave was the best predictor of risk of local recurrence, and a model based on BEDmin had substantially less evidential support. In univariable logistic regression, the model based on BEDave also performed best. Multivariable classification using fast and frugal trees revealed BEDmax to be the most important predictor, followed by BEDave.

CONCLUSIONS

BEDave was generally better correlated with tumor control probability than either BEDmax or BEDmin. Because the average between near-minimum and near-maximum doses was highly correlated to the mean gross tumor volume dose, the latter may be used as a prescription target. More emphasis could be placed on achieving sufficiently high mean doses within the gross tumor volume rather than the PTV covering dose, a concept needing further validation.

Feasibility of cone beam CT-guided library of plans strategy in pre-operative gastric cancer radiotherapy

BACKGROUND AND PURPOSE

The stomach displays large anatomical changes in size, shape and position, which implies the need for plan adaptation for gastric cancer patients who receive pre-operative radiotherapy. We evaluated the feasibility and necessity of a CBCT-guided library of plans (LoP) strategy in gastric cancer radiotherapy.

METHODS

Eight gastric cancer patients treated with 24-25 fractions of single-plan radiotherapy with daily CBCT imaging were included. The target was delineated on the pre-treatment CT and first 5 CBCTs to create a patient-specific LoP. Plan selections were performed by 12 observers in a training stage (2-3 CBCTs per patient) and an assessment stage (17 CBCTs per patient). The observers were asked to select the smallest plan that encompassed the target on the CBCT. A total of 136 plan selections were evaluated in the assessment stage.

RESULTS

Delineations on CBCTs showed that in 90% of the 40 delineated fractions part of the CTV was outside the PTV based on the pre-treatment CT. At least two-thirds of the observers agreed on the selected plan in 65.2% and 70% of the fractions in the training stage and the assessment stage, respectively. For each patient, at least two different plans from the LoP were the most selected plan.

CONCLUSION

A CBCT-guided patient-specific LoP strategy is feasible for gastric cancer patients, yielding good agreement in plan selections. Unless generous margins are used to avoid frequent geometric misses, it is likely that part of the target will be missed with single-plan radiotherapy.

Accurate estimation of daily delivered radiotherapy dose with an external treatment planning system

Accurate estimation of the daily radiotherapy dose is challenging in a multi-institutional collaboration when the institution specific treatment planning system (TPS) is not available. We developed and evaluated a method to tackle this problem. Residual errors in daily estimations were minimized with single correction based on the planned dose. For nine patients, medians of the absolute estimation errors for targets and OARs were less than 0.2 Gy (Dmean), 0.3 Gy (D1), and 0.1 Gy (D99). In general, mimicking errors were significantly smaller than dose differences caused by anatomical changes. The demonstrated accuracy may facilitate dose accumulation in a multi-institutional/multi-vendor setting.