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.

Patient-reported outcomes after personalised dose-escalation for stage II-III non-small-cell lung cancer patients: Results from the randomised ARTFORCE PET-Boost trial.

BACKGROUND AND PURPOSE: The ultimate challenge in dose-escalation trials lies in finding the balance between benefit and toxicity. We examined patient-reported outcomes (PROs), including health-related quality of life (HRQoL) in patients with locally advanced non-small cell lung cancer (LA-NSCLC), treated with dose-escalated radiotherapy. MATERIALS AND METHODS: The international, randomised, phase 2 ARTFORCE PET-Boost study (NCT01024829) aimed to improve 1-year freedom from local failure rates in patients with stage II-III NSCLC, with a ≥ 4 cm primary tumour. Treatment consisted of an individualised, escalated fraction dose, either to the primary tumour as a whole or to its most FDG-avid subvolume (24 x 3.0-5.4 Gy). Patients received sequential or concurrent chemoradiotherapy, or radiotherapy only. Patients were asked to complete the EORTC QLQ-C30, QLQ-LC13, and the EuroQol-5D at eight timepoints. We assessed the effect of dose-escalation on C30 sum score through mixed-modelling and evaluated clinically meaningful changes for all outcomes. RESULTS: Between Apr-2010 and Sep-2017, 107 patients were randomised; 102 were included in the current analysis. Compliance rates: baseline 86.3%, 3-months 85.3%, 12-months 80.3%; lowest during radiation treatment 35.0%. A linear mixed-effect (LME) model revealed no significant change in overall HRQoL over time, and no significant difference between the two treatment groups. Physical functioning showed a gradual decline in both groups during treatment and at 18-months follow-up, while clinically meaningful worsening of dyspnoea was seen mainly at 3- and 6-months. CONCLUSION: In patients with LA-NSCLC treated with two dose-escalation strategies, the average patient-reported HRQoL remained stable in both groups, despite frequent patient-reported symptoms, including dyspnoea, dysphagia, and fatigue.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 CTVstomach (i.e., stomach + 10 mm uniform margin minus OARs) and CTVLN (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 CTVstomach (SP and LoP, separately) and CTVLN. 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 PTVstomach 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.

18F-FDG-PET guided vs whole tumour radiotherapy dose escalation in patients with locally advanced non-small cell lung cancer (PET-Boost): Results from a randomised clinical trial.

BACKGROUND AND PURPOSE: We aimed to assess if radiation dose escalation to either the whole primary tumour, or to an 18F-FDG-PET defined subvolume within the primary tumour known to be at high risk of local relapse, could improve local control in patients with locally advanced non-small-cell lung cancer. MATERIALS AND METHODS: Patients with inoperable, stage II-III NSCLC were randomised (1:1) to receive dose-escalated radiotherapy to the whole primary tumour or a PET-defined subvolume, in 24 fractions. The primary endpoint was freedom from local failure (FFLF), assessed by central review of CT-imaging. A phase II 'pick-the-winner' design (alpha = 0.05; beta = 0.80) was applied to detect a 15 % increase in FFLF at 1-year. CLINICALTRIALS: gov:NCT01024829. RESULTS: 150 patients were enrolled. 54 patients were randomised to the whole tumour group and 53 to the PET-subvolume group. The trial was closed early due to slow accrual. Median dose/fraction to the boosted volume was 3.30 Gy in the whole tumour group, and 3.50 Gy in the PET-subvolume group. The 1-year FFLF rate was 97 % (95 %CI 91-100) in whole tumour group, and 91 % (95 %CI 82-100) in the PET-subvolume group. Acute grade ≥ 3 adverse events occurred in 23 (43 %) and 20 (38 %) patients, and late grade ≥ 3 in 12 (22 %) and 17 (32 %), respectively. Grade 5 events occurred in 19 (18 %) patients in total, of which before disease progression in 4 (7 %) in the whole tumour group, and 5 (9 %) in the PET-subvolume group. CONCLUSION: Both strategies met the primary objective to improve local control with 1-year rates. However, both strategies led to unexpected high rates of grade 5 toxicity. Dose differentiation, improved patient selection and better sparing of central structures are proposed to improve dose-escalation strategies.

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 POcc  = 0.50 and 50 other isosurfaces (from POcc  = 0.01 to 0.99), with POcc 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 (SDdeform ) expressing local deformation. For each volunteer, we determined median and 98th percentile of SDdeform over the reference surface and compared these with the respiratory-induced displacement SDresp , 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 SDdeform (range, 2.0-2.9 mm) was smaller than SDresp (2.7-8.8 mm) for each volunteer (p < 0.00001); 98th percentile of SDdeform (3.2-7.3 mm) did not significantly differ from SDresp (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 (3DRef) was optimized on a 3D mid-position CT. To estimate the dosimetric impact of respiratory motion (4DRef), inter-fractional variations (3DAcc) and the combined effect of both factors (4DAcc), 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 3DRef.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.