Adaptive Radiotherapy for Anatomical Changes

Improving patient specific quality assurance for image registration: clinical use case of target contouring for PET deformable image registration

Deformable image registration (DIR) has proven to be an invaluable tool to maximize the clinical benefits of multimodality imaging in radiation oncology. In contrast to rigid image registration (RIR), which is employed at all stages of diagnosis and treatment, the uptake of DIR has been constrained by concerns over the potential for unsafe use. The AAPM Task Group 132 (TG132) published a report on the use of image registration, including many recommendations on clinical integration of registration in treatment planning and delivery. There is a remaining uncertainty on incorporating registration uncertainties into treatment margins (Sect. 6.A, TG 132), a challenge in clinical practice. The aim of this work was to report our experience in implementing a practical, patient specific quality assurance process based on the AAPM Task Group 132 report recommendations. This work includes refining our process of target contouring using PET with deformable image registration based on our experience of addressing vulnerabilities identified during implementation. A multidisciplinary team created a flowchart for patient specific quality assurance for image registration (RIR or DIR) based on use cases defined in the AAPM TG132 Report on the use of image registration in radiotherapy. Vulnerabilities identified from this implementation were assessed relative to AAPM TG132 recommendations. These findings were used to adapt our patient specific quality assurance to mitigate vulnerabilities. The main vulnerabilities were identified in the last steps of image registration. There was potential for inappropriate use of the registration for clinical use, such as target contouring where the image registration accuracy level was poor. Vulnerabilities were addressed by an adaptation in our quality assurance process. A new physics image registration QA task was introduced that independently checks registration accuracy and appropriateness of target contouring, addressing the vulnerability in the last steps of the AAPM TG132 flowchart. A multi-disciplinary team implemented the image registration process outlined by AAPM TG132. An improved patient specific quality assurance process was developed by introducing an independent physics image registration review that considers the acceptable registration uncertainty for the specific clinical use case in question.

Clinical feasibility of treatment planning on a diagnostic CT scan without or with single fraction plan adaptation in patients with stage II/III rectal cancer

BACKGROUND

With the ultimate aim of reducing time to start radiotherapy treatment in patients with rectal cancer, this study explores the feasibility of omitting a planning CT scan (pCT), by utilizing the diagnostic CT scan (dCT) for treatment planning, with or without plan adaption using online adaptive radiotherapy.

METHODS

Fifteen rectal cancer patients, with both dCT and pCT available, were included. Target volumes and organs at risk (OARs) were delineated on both scans, followed by treatment planning based on the dCT contours. Plans were recalculated on the pCT to assess dosimetric differences for target volumes and OARs. Additionally, five patients with HyperSight CBCT scans underwent a similar planning process. An online adaptive treatment workflow was simulated using the Ethos system, where the dCT and its plan served as the reference, and the HyperSight CBCT was used for adaptation.

RESULTS

dCT-based plans showed adequate target volume coverage. However, when recalculated on the pCT, median coverage decreased for both CTV and PTV, and OAR doses increased. None of the 15 plans met prescribed constraints without online adaptive radiotherapy. In contrast, for all five patients in the adaptive workflow, the treatment plans met target volume coverage and OAR constraints.

CONCLUSION

Using dCT-based treatment planning is feasible for rectal cancer patients but requires at least one online adaptive session. A prospective trial (MEC 2023-0445) is ongoing in patients with rectal cancer, aiming to reduce time to start treatment, by omitting the pCT and using online adaptive radiotherapy workflow.

Clinical Practice-Based Failure Modes and Root Cause Analysis of Cone Beam CT-Guided Online Adaptive Radiotherapy of the Pelvis

Background/Objectives: Cone-beam computed tomography (CBCT)-guided online adaptive radiotherapy (oART) represents a significant advancement in radiation oncology, enabling on-couch plan adaptation to account for daily anatomical changes. While this automation improves precision and workflow efficiency, it also introduces new failure modes (FMs) and workflow irregularities. This study aimed to systematically evaluate the clinical and technical challenges associated with CBCT-guided oART implementation. Methods: We retrospectively analyzed over 1000 CBCT-guided oART sessions for pelvic malignancies performed at our institution. A multidisciplinary team conducted a comprehensive review to identify and classify FMs, followed by root cause analysis (RCA) to evaluate their impact on treatment safety, efficacy, and workflow robustness. Results: In addition to session-terminating FMs, we identified recurring failure modes across three major domains: (1) system-driven issues, such as rigid target localization and software-driven irregularities; (2) patient-driven challenges, including interfractional and intrafractional anatomical variations; and (3) treatment planning and execution failures, including excessive dose hotspots from field-of-view limitations. The system's closed-loop automation, while streamlining processes, introduced rigid constraints in plan adaptation and fallback plan execution, occasionally leading to unintended dose discrepancies. Conclusions: This study provides a comprehensive clinical practice-based evaluation of CBCT-guided oART, highlighting system-specific failure modes and their implications. Addressing these challenges requires structured quality assurance processes, multidisciplinary collaboration, and continuous workflow refinement. Our findings contribute to the development of safer and more robust adaptive radiotherapy platforms and clinical workflows.

Quantitative use of cone-beam computed tomography in proton therapy: challenges and opportunities

The fundamental goal in radiation therapy (RT) is to simultaneously maximize tumor cell killing and healthy tissue sparing. Reducing uncertainty margins improves normal tissue sparing, but generally requires advanced techniques. Adaptive RT (ART) is a compelling technique that leverages daily imaging and anatomical information to support reduced margins and to optimize plan quality for each treatment fraction. An especially exciting avenue for ART is proton therapy (PT), which aims to combine daily plan re-optimization with the unique advantages provided by protons, including reduced integral dose and near-zero dose deposition distal to the target along the beam direction. A core component for ART is onboard image guidance, and currently two options are available on proton systems, including cone-beam computed tomography (CBCT) and CT-on-rail (CToR) imaging. While CBCT suffers from poorer image quality compared to CToR imaging, CBCT platforms can be more easily integrated with PT systems and thus may support more streamlined adaptive proton therapy (APT). In this review, we present current status of CBCT application to proton therapy dose evaluation and plan adaptation, including progress, challenges and future directions.

Efficient proton transport modelling for proton beam therapy and biological quantification

In this work, we present a fundamental mathematical model for proton transport, tailored to capture the key physical processes underpinning Proton Beam Therapy (PBT). The model provides a robust and computationally efficient framework for exploring various aspects of PBT, including dose delivery, linear energy transfer, treatment planning and the evaluation of relative biological effectiveness. Our findings highlight the potential of this model as a complementary tool to more complex and computationally intensive simulation techniques currently used in clinical practice.

Identifiability and model selection frameworks for models of high-grade glioma response to chemoradiation

We have developed a family of biology-based mathematical models of high-grade glioma (HGG), capturing the key features of tumour growth and response to chemoradiation. We now seek to quantify the accuracy of parameter estimation and determine, when given a virtual patient cohort, which model was used to generate the tumours. In this way, we systematically test both the parameter and model identifiability. Virtual patients are generated from unique growth parameters whose growth dynamics are determined by the model family. We then assessed the ability to recover model parameters and select the model used to generate the tumour. We then evaluated the accuracy of predictions using the selected model at four weeks post-chemoradiation. We observed median parameter errors from 0.04% to 72.96%. Our model selection framework selected the model that was used to generate the data in 82% of the cases. Finally, we predicted the growth of the virtual tumours using the selected model resulting in low error at the voxel-level (concordance correlation coefficient (CCC) ranged from 0.66 to 0.99) and global level (percentage error in total tumour cellularity ranged from -12.35% to 0.07%). These results demonstrate the reliability of our framework to identify the most appropriate model under noisy conditions expected in the clinical setting.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

Deep learning-based Monte Carlo dose prediction for heavy-ion online adaptive radiotherapy and fast quality assurance: A feasibility study

BACKGROUND

Online adaptive radiotherapy (OART) and rapid quality assurance (QA) are essential for effective heavy ion therapy (HIT). However, there is a shortage of deep learning (DL) models and workflows for predicting Monte Carlo (MC) doses in such treatments.

PURPOSE

This study seeks to address this gap by developing a DL model for independent MC dose (MCDose) prediction, aiming to facilitate OART and rapid QA implementation for HIT.

METHODS AND MATERIALS

A MC dose prediction DL model called CAM-CHD U-Net for HIT was introduced, based on the GATE/Geant4 MC simulation platform. The proposed model improved upon the original CHD U-Net by adding a Channel Attention Mechanism (CAM). Two experiments were conducted, one with CHD U-Net (Experiment 1) and another with CAM-CHD U-Net (Experiment 2), and involved data from 120 head and neck cancer patients. Using patient CT images, three-dimensional energy matrices, and ray-masks as inputs, the model completed the entire MC dose prediction process within a few seconds.

RESULTS

In Experiment 2, within the Planned Target Volume (PTV) region, the average gamma passing rate (3%/3 mm) between the predicted dose and true MC dose reached 99.31%, and 96.48% across all body voxels. Experiment 2 demonstrated a 46.15% reduction in the mean absolute difference inD 5 ${D_5}$ in organs at risk compared to Experiment 1.

CONCLUSIONS

By extracting relevant parameters of radiotherapy plans, the CAM-CHD U-Net model can directly and accurately predict independent MC dose, and has a high gamma passing rate with the ground truth dose (the dose obtained after a complete MC simulation). Our workflow enables the implementation of heavy ion OART, and the predicted MCDose can be used for rapid QA of HIT.

A dosimetric comparison of helical tomotherapy treatment delivery with real-time adaption and no motion correction

This study assesses the ability of a helical tomotherapy system equipped with kV imaging and optical surface guidance to adapt to motion traces in real-time. To assess the delivery accuracy with motion, a unified testing framework was used. The average 2 %/2 mm γ-fail rates across all lung traces were 0.1 % for motion adapted and 17.4 % for no motion correction. Average 2 %/2 mm γ-fail rates across all prostate traces were 0.4 % for motion adapted and 12.2 % for no motion correction. Real-time motion adaption was shown to improve the accuracy of dose delivered to a moving phantom compared with no motion adaption. MeSH Keywords: Radiotherapy, image-guided; Radiation therapy, targeted.

Introducing a novel sub-millimeter lung CT image registration error quantitation tool

BACKGROUND

Lung computed tomography (CT) scan image registration is being used for lung function analysis such as ventilation. Given the high sensitivity of functional analyses to image registration errors, an image registration error scoring tool that can measure submillimeter image registration errors is needed.

PURPOSE

To propose an image registration error scoring tool, termed λ, whose spatial sensitivity can be used to quantify image registration errors in steep image gradient regions under realistic noise conditions.

METHODS

λ compares two images, termed reference and evaluated. The HU and distance scales of both images are normalized by user-selected scaling criteria. For each voxel in the reference image, the 4D Euclidian distances between the reference voxel and the nearby evaluated voxels are calculated, and the minimum of these distances is λ $\lambda $ . We tested λ $\lambda $ in simulated individual blood vessels comprised of 1, 3, and 5 mm diameter cylinders in 1 × 1 × 1 mm3 voxel images, which were blurred to simulate CT scanner intrinsic resolution and volume averaging. We placed the simulated vessels in a homogeneous background simulating parenchymal tissue density and injected 20, 40, and 60 HU standard deviation Gaussian noise. We used isotropic Gaussian filters with 0.5, 1.0, and 1.5 mm standard deviation kernels to smooth the simulated images. We assessed λ $\lambda $ using reference-evaluated vessel shifts of -1.0 to 1.0 mm in 0.05 mm steps via rigid translational and rotational deformations. We examined whether λ $\lambda $ tracked the translation vector via its internal spatial component. We restricted λ $\lambda $ to voxels using the angle, termed θ $\theta $ , between the λ $\lambda $ vector and the normalized spatial-distance axes, terming the results the restricted- λ $\lambda $ ,λ R ${{\lambda }_R}$ , where θ $\theta $ was hypothesized to be a proxy for image gradient. We determined whether θ $\theta $ was coincident with the image gradient by examining if the voxels with| θ | ≤ 30 ∘ $| \theta | \le {{30}^ \circ }$ tracked the evaluated vessels. We used the 95th percentile ofλ R ${{\lambda }_R}$ ,λ R 95 $\lambda _R^{95}$ , to determine spatial sensitivity, which we took as a conservative estimate of registration error, by fittingλ R 95 $\lambda _R^{95}$ to a modified absolute-value function for each tested rigid translation, noise level, smoothing kernel, and vessel radius combination. We demonstrated the use of λ $\lambda $ on a clinical example consisting of a set of 25 deformably registered free-breathing thoracic CT scans. We visually compared the λ $\lambda $ andλ R ${{\lambda }_R}$ results against the HU differences between each clinical image pair.

RESULTS

We found θ to be coincident with the image gradient. We found that λ $\lambda $ 's spatial component tracked the vessel shifts. We determined the spatial sensitivity limit ofλ R 95 $\lambda _R^{95}$ to be < 0.2 mm. The noise level and smoothing kernel influencedλ R 95 $\lambda _R^{95}$ sensitivity, worsening with increasing noise, and improving with increasing smoothing. For the clinical images, we observed λ $\lambda $ to qualitatively match the absolute difference of intensity in the image pairs andλ R ${{\lambda }_R}$ to restrict itself to high gradient regions or regions of visually apparent errors.

CONCLUSION

λ R 95 $\lambda _R^{95}$ detected sub-millimeter positioning errors between simulated vessels in the presence of typical CT noise. The noise magnitude and choice of noise smoothing kernel were inversely related toλ R 95 $\lambda _R^{95}$ sensitivity, implying that study-specific tuning of the pre-smoothing kernel may be required. The demonstrated ability in geometric tests ofλ R 95 $\lambda _R^{95}$ to detect subvoxel DIR errors warrants further evaluation and testing.

Training for tomorrow: Establishing a worldwide curriculum in online adaptive radiation therapy

This commentary discusses the implementation of online adaptive radiation therapy (oART) in cancer treatment within the context of current challenges faced by radiation therapy professionals. oART enables modifications to treatment plans based on daily imaging, enhancing target accuracy while minimising harm to surrounding organs. Despite its potential to improve patient outcomes, the application of oART is hindered by notable barriers, particularly in human resources. A global shortage of skilled radiation professionals such as radiation therapists or therapeutic radiographers (RTTs), medical physicists and radiation oncologists, along with training challenges in online adaptive techniques, hinders oART implementation and sustainability. Moreover, geographical disparities limit access to advanced training programs, leaving RTTs and their patients in underserved regions at a disadvantage. There is growing global evidence that RTTs are being successfully trained to lead adaptive fractions in both cone-beam computed tomography and magnetic resonance imaging guided oART. This commentary proposes the notion of standards for a global training curriculum to address barriers and expand RTT capabilities in delivering oART. By leveraging artificial intelligence and fostering interdisciplinary collaboration, the radiation therapy field can enhance efficiency and accuracy in oART. Successful training models from leading institutions illustrate the importance of hands-on experience and ongoing mentorship. A coordinated effort among stakeholders is essential to establish a comprehensive global training framework, ultimately improving patient access to oART and elevating standards of care worldwide.

Deep learning based super-resolution for CBCT dose reduction in radiotherapy

BACKGROUND

Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction.

PURPOSE

To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT.

METHODS

An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test.

RESULTS

Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4 HU/2.9 HU/40.4 dB/0.82 for CBCTLR, 136.6 HU/-0.4 HU/38.6 dB/0.77 for CBCTSRpro, and 128.2 HU/1.9 HU/39.0 dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66 lp/mm, CBCTSRpro: 0.88 lp/mm, CBCTSRimg: 0.95 lp/mm, CBCTHR: 1.01 lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06 mm for translations (CBCTHR as reference).

CONCLUSIONS

This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain.

Monte Carlo dose calculation for photon and electron radiotherapy on dynamically deforming anatomy

BACKGROUND

Dose calculation in radiotherapy aims to accurately estimate and assess the dose distribution of a treatment plan. Monte Carlo (MC) dose calculation is considered the gold standard owing to its ability to accurately simulate particle transport in inhomogeneous media. However, uncertainties such as the patient's dynamically deforming anatomy can still lead to differences between the delivered and planned dose distribution.

PURPOSE

Development and validation of a deformable voxel geometry for MC dose calculations (DefVoxMC) to account for dynamic deformation in the dose calculation process of photon- and electron-based radiotherapy treatment plans for clinically motivated cases.

METHODS

DefVoxMC relies on the subdivision of a regular voxel geometry into dodecahedrons. It allows shifting the dodecahedrons' corner points according to the deformation in the patient's anatomy using deformation vector fields (DVF). DefVoxMC is integrated into the Swiss Monte Carlo Plan (SMCP) to allow the MC dose calculation of photon- and electron-based treatment plans on the deformable voxel geometry. DefVoxMC is validated in two steps. A compression test and a Fano test are performed in silico. Delta4 (for photon beams) and EBT4 film measurements in a cubic PMMA phantom (for electron beams) are performed on a TrueBeam in Developer Mode for clinically motivated treatment plans. During these measurements, table motion is used to mimic rigid dynamic patient motion. The measured and calculated dose distributions are compared using gamma passing rate (GPR) (3% / 2 mm (global), 10% threshold). DefVoxMC is used to study the impact of patient-recorded breathing motion on the dose distribution for clinically motivated lung and breast cases, each prescribed 50 Gy to 50% of the target volume. A volumetric modulated arc therapy (VMAT) and an arc mixed-beam radiotherapy (Arc-MBRT) plan are created for the lung and breast case, respectively. For the dose calculation, the dynamic deformation of the patient's anatomy is described by DVFs obtained from deformable image registration of the different phases of 4DCTs. The resulting dose distributions are compared to the ones of the static situation using dose-volume histograms and dose differences.

RESULTS

DefVoxMC is successfully integrated into the SMCP to enable the MC dose calculation of photon- and electron-based treatments on a dynamically deforming patient anatomy. The compression and the Fano test agree within 1.0% and 0.1% with the expected result, respectively. Delta4 and EBT4 film measurements agree with the calculated dose by a GPR >95%. For the clinically motivated cases, breathing motion resulted in areas with a dose increase of up to 26.9 Gy (lung) and up to 7.6 Gy (breast) compared to the static situation. The largest dose differences are observed in high-dose-gradient regions perpendicular to the beam plane, consequently decreasing the planning target volume coverage (V95%) by 4.2% for the lung case and 2.0% for the breast case.

CONCLUSIONS

A novel method for MC dose calculation for photon- and electron-based treatments on dynamically deforming anatomy is successfully developed and validated. Applying DefVoxMC to clinically motivated cases, we found that breathing motion has non-negligible impact on the dosimetric plan quality.

Augmenting motion artifacts to enhance auto-contouring of complex structures in cone-beam computed tomography imaging

Objective. To develop an augmentation method that simulates cone-beam computed tomography (CBCT) related motion artifacts, which can be used to generate training-data to increase the performance of artificial intelligence models dedicated to auto-contouring tasks.Approach.The augmentation technique generates data that simulates artifacts typically present in CBCT imaging. The simulated pseudo-CBCT (pCBCT) is created using interleaved sequences of simulated breath-hold and free-breathing projections. Neural networks for auto-contouring of head and neck and bowel structures were trained with and without pCBCT data. Quantitative and qualitative assessment was done in two independent test sets containing CT and real CBCT data focus on four anatomical regions: head, neck, abdomen, and pelvis. Qualitative analyses were conducted by five clinical experts from three different healthcare institutions.Main results.The generated pCBCT images demonstrate realistic motion artifacts comparable to those observed in real CBCT data. Training a neural network with CT and pCBCT data improved Dice similarity coefficient (DSC) and average contour distance (ACD) results on CBCT test sets. The results were statistically significant (p-value ⩽.03) for bone-mandible (model without/with pCBCT: 0.91/0.92 DSC,p⩽ .01; 0.74/0.66 mm ACD,p⩽.01), brain (0.34/0.93 DSC,p⩽ 1 × 10-5; 17.5/2.79 mm ACD,p= 1 × 10-5), oral-cavity (0.81/0.83 DSC,p⩽.01; 5.11/4.61 mm ACD,p= .02), left-submandibular-gland (0.58/0.77 DSC,p⩽.001; 3.24/2.12 mm ACD,p⩽ .001), right-submandibular-gland (0.00/0.75 DSC,p⩽.1 × 10-5; 17.5/2.26 mm ACD,p⩽ 1 × 10-5), left-parotid (0.68/0.78 DSC,p⩽ .001; 3.34/2.58 mm ACD,p⩽.01), large-bowel (0.60/0.75 DSC,p⩽ .01; 6.14/4.56 mm ACD,p= .03) and small-bowel (3.08/2.65 mm ACD,p= .03). Visual evaluation showed fewer false positives, false negatives, and misclassifications in artifact-affected areas. Qualitative analyses demonstrated that, auto-generated contours are clinically acceptable in over 90% of cases for most structures, with only a few requiring adjustments.Significance.The inclusion of pCBCT improves the performance of trainable auto-contouring approaches, particularly in cases where the images are prone to severe artifacts.

Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region

Background and purpose

A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.

Materials and methods

Twenty prostate cancer patients were enrolled in this prospective clinical study. Per patient, one pair of high-performance CBCT and conventional CBCT scans was included. Three observers manually corrected contours generated by the artificial intelligence (AI) model for prostate, seminal vesicles, bladder, rectum and bowel. Differences between AI-based and manual corrected contours were quantified using Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Autosegmentation performance and interobserver variation were compared using a random effects model; correction times and confidence scores using a paired t-test and Wilcoxon signed-rank test, respectively.

Results

Autosegmentation performance showed small, but statistically insignificant differences. Interobserver variability, assessed by the intraclass correlation coefficient, was significantly different across most organs, but these were considered clinically irrelevant (maximum difference = 0.08). Mean contour correction times were similar for both CBCT systems (11:03 versus 11:12 min; p = 0.66). Delineation confidence scores were significantly higher with the high-performance CBCT scans for prostate, seminal vesicles and rectum (4.5 versus 3.5, 4.3 versus 3.5, 4.8 versus 4.3; all p < 0.001).

Conclusion

The high-performance CBCT did not (clinically) improve autosegmentation performance, inter-observer variability or contour correction time compared to conventional CBCT. However, it clearly enhanced user confidence in organ delineation for prostate, seminal vesicles and rectum.

A feasibility study of dose-band prediction in radiation therapy: Predicting a spectrum of plan dose

PURPOSE

The current deep learning-based dose prediction methods only predict one dose distribution. If the predicted dose is inaccurate, no additional options can be selected. To overcome this limitation, we propose a novel dose prediction method called "dose-band prediction," which provides a spectrum of predicted dose distributions for planning and quality assurance (QA) purposes.

MATERIAL AND METHODS

We utilized Upper/Lower-band losses in 3D neural networks to establish the Upper/Lower-band models (UBM/LBM). The maximum/minimum rational dose predicted in UBM/LBM defined the ideal dose spectrum for each voxel. We enrolled 104 nasopharyngeal carcinoma cases with tomotherapy (dataset 1), 54 cervical carcinoma cases with IMRT (dataset 2), and 37 cervical carcinoma cases with VMAT (dataset 3) in the study. Moreover, a dose band-based auto planning (Auto-plandose-band) attempt was carried out in dataset 3, compared with the MSE model (Auto-planMSE).

RESULTS

The UBM/LBM doses tend to be higher/lower than the clinical dose, forming a predicted dose spectrum. The Middle-line dose represents the average of the Upper/Lower-band, which was consistent with the clinical dose. The mean differences of the planning target volumes (PTVs) and organs at risk (OARs) for the Upper-band, Middle-line, and Lower-band in Dataset 1 were 3.66 %, -0.40 %, and -4.48 % in Dataset 2, they were 2.40 %, -1.62 %, and -5.57 %; in Dataset 3, they were 2.18 %, -0.59 %, and -3.31 %. When PTVs meet prescription, the mean difference between Auto-plandose-band and Auto-planMSE in OARs was -2.67 %.

CONCLUSION

The dose-band prediction successfully predicted a spectrum of doses, making auto-planning and QA flexible and high quality.

Clinical Implementation of Cone Beam Computed Tomography-Guided Online Adaptive Radiation Therapy in Whole Breast Irradiation

Purpose

In postoperative breast irradiation, changes in the breast contour and arm positioning can result in patient positioning errors and offline replanning. This can lead to increased treatment burden and strain on departmental logistics because of the need for additional cone beam computed tomography (CBCT) images or even a new radiation therapy treatment plan (TP). Online daily adaptive radiation therapy (oART) could provide a solution to these challenges. We have clinically implemented and evaluated the feasibility of oART for whole breast irradiation.

Methods and Materials

Twenty patients treated with postoperative whole breast right irradiation (5 × 5.2 Gy) were included in BREAST-ART, a prospective single-arm trial. The dosimetry of the reference TP calculated on the daily anatomy and adaptive TP were compared. Duration of the oART workflow, in-house satisfaction questionnaires, and acute toxicity (National Cancer Institute Common Terminology Criteria for Adverse Event v5.0) were collected. The oART workflow was evaluated by investigating the impact of manual corrections of influencer and target contours on treatment time and quality.

Results

In the first 17 patients (85 fractions), the on-couch time, ie, the time between the end of CBCT1 and CBCT3, was a median of 13.8 minutes (range, 11-25). Retrospective evaluation of the use of the influencer (ie, breast) in 4 patients (20 fractions) and manual correction of the most cranial and caudal target contours (ie, 4 mm) in 10 patients (36 fractions) was done. This resulted in a reduced on-couch time in the last 3 clinical patients to a median of 13.0 minutes (range, 11-19). No grade 3 or higher toxicity was observed, and 19 of 20 patients indicated that they preferred the same treatment again. Skin marks for patient positioning during treatment were no longer necessary.

Conclusions

This study showed the feasibility, challenges, and practical solutions for the implementation of oART for breast cancer patients. Future work will focus on more complex breast indications, such as whole breast, including axillary nodes, to further investigate the benefits and challenges of oART in breast cancer.

Dose calculation accuracy of a new high-performance ring-gantry CBCT imaging system for prostate and lung cancer patients

BACKGROUND AND PURPOSE

The recently introduced high-performance CBCT imaging system called HyperSight offers improved Hounsfield units (HU) accuracy, a larger CBCT field-of-view and improved image quality compared to conventional ring gantry CBCT, possibly enabling treatment planning on CBCT imaging directly. In this study, we evaluated whether the dose calculation accuracy on HyperSight CBCT was sufficient for treatment planning in prostate and lung cancer patients.

MATERIALS AND METHODS

HyperSight CBCT was compared to planning CT (pCT) in terms of HU-to-mass density (MD) calibration curves. For twenty prostate patients and twenty lung patients, differences in DVH parameters, and 3D global gamma between dose distributions calculated on pCT and free breathing HyperSight CBCT were evaluated. For this purpose, HyperSight CBCT acquired at the first fraction was rigidly registered to the pCT, delineations from the CT were propagated and the dose was recalculated on the HyperSight CBCT.

RESULTS

For each insert of the HU-to-MD calibration phantom, the HU values of HyperSight CBCT and pCT agreed within 35 HU. For prostate maximum deviations in PTV Dmean, V95% and V107% were 1.8 %, -1.1 % and < 0.1 % respectively. For lung PTV V95% was generally lower (median -1.1 %) and PTV V107% was generally higher (median 1.1 %) on HyperSight CBCT due to breathing motion artifacts. The average (±SD) 2 %/2mm gamma pass rate was 98.7 %±1.2 % for prostate cancer patients and 96.2 %±2.1 % for lung cancer patients.

CONCLUSION

HyperSight CBCT enabled accurate dose calculation for prostate cancer patients, without implementation of a specific HyperSight CBCT-to-MD curve. For lung cancer patients, breathing motion hampered accurate dose calculations.

An End-to-end Quality Assurance Procedure for Ethos Online Adaptive Radiotherapy

Purpose

Online adaptive radiation therapy (OART) poses unique challenges for quality assurance (QA), requiring innovative methodologies beyond traditional techniques. This study introduced an end-to-end (E2E) QA test for the Ethos OART system.

Materials and Methods

Initial treatment plans were developed using deformed computed tomography (CT) images of standard phantoms. During treatment sessions, adaptive plans were created and delivered using undistorted physical QA phantoms equipped with measuring detectors. Our approach was demonstrated using standard QA phantoms - OCTAVIUS-four-dimensional (PTW, Freiburg, Germany), ArcCHECK (Sun Nuclear Corp., FL, USA), and the RUBY (PTW, Freiburg, Germany) - to evaluate the accuracy of contouring, synthetic CT (sCT), and dosimetry of adaptive plans in the Ethos OART system.

Results

Our findings demonstrated the superior performance of the Ethos OART system, with a gamma pass rate exceeding 96% (2% local/2 mm) and point dose deviations below 0.5%. The Dice coefficients for body contours between the sCT and reference CT were above 0.9, and the sCT accuracy was confirmed by mean absolute errors of <27 Hounsfield unit.

Conclusion

This approach establishes a straightforward E2E test to assess the workflow accuracies essential for preclinical validation/monthly QA of OART systems.

Thermoradiotherapy Optimization Strategies Accounting for Hyperthermia Delivery Uncertainties

PURPOSE

The combined effect of hyperthermia and radiation therapy can be quantified by an enhanced equivalent radiation dose (EQDRT). Uncertainties in hyperthermia treatment planning and adjustments during treatment can impact achieved EQDRT. We developed and compared strategies for EQDRT optimization of radiation therapy plans, focusing on robustness against common adjustments.

METHODS AND MATERIALS

Using Plan2Heat, we computed preplanning hyperthermia plans and treatment adjustment scenarios for 3 cervical cancer patients. We imported these scenarios into RayStation 12A for optimization with 4 different strategies: (1) conventional radiation therapy optimization prescribing 46 Gy to the planning target volume (PTV), (2) nominal EQDRT optimization using the preplanning scenario, targeting uniform 58 Gy in the gross tumor volume (GTV), keeping organs at risk doses as in plan 1, (3) robust EQDRT optimization, as plan 2 but adding adjusted scenarios for optimization, and (4) library of plans (4 plans) with strategy 2 criteria but optimizing on 1 adjusted scenario per plan. We calculated for each radiation therapy plan EQDRT distributions for preplanning and adjusted scenarios, evaluating each combination of GTV coverage and homogeneity objectives.

RESULTS

EQDRT95% increased from 49.9 to 50.9 Gy in strategy 1 to 56.1 to 57.4 Gy in strategy 2 with the preplanning scenario, improving homogeneity by ∼10%. Strategy 2 demonstrated the best overall robustness, with 62% of all GTV objectives within tolerance. Strategy 3 had a higher percentage of coverage objectives within tolerance than strategy 2 (68% vs 54%) but a lower percentage for uniformity (44% vs 71%). Strategy 4 showed a similar EQDRT95% and homogeneity for adjusted scenarios than strategy 2 for a preplanning scenario. D0.1% (radiation dose received by the 0.1% most irradiated volume) for organs at risk was increased by strategies 2 to 4 by up to ∼6 Gy.

CONCLUSIONS

EQDRT optimization enhances EQDRT levels and uniformity compared with conventional optimization. Better overall robustness is achieved by optimizing the preplanning hyperthermia plan. Robust optimization improves coverage but reduces homogeneity. A library of plans ensures coverage and uniformity when dealing with adjusted hyperthermia scenarios.

A code orange for traffic-light-protocols as a communication mechanism in IGRT

Introduction

Traffic-light protocols (TLPs) use color codes to standardize image registration and improve interdisciplinary communication in IGRT. Generally, green indicates no relevant anatomical changes, orange signals changes requiring follow-up but does not compromise the current fraction, and red flags unacceptable changes. This study examines the communication aspect, specifically the reporting accuracy for locally advanced non-small-cell lung cancer (LA-NSCLC), and identifies barriers to reporting.

Materials & Methods

We conducted a retrospective study on 1997 CBCTs from 74 LA-NSCLC patients. Each scan was in retrospect assessed blinded using the tailored TLP by an IGRT-RTT and subsequently by a second RTT for a subset of fractions. The assessment included both CBCTs from current clinical practice (TLP2023) and from the TLP implementation period (TLP2019). Accuracy of image registration was not evaluated. Reporting barriers were identified through focus group discussions with RTTs.

Results

During TLP2023, 22 of the 63 (35%) patients received at least one code orange during therapy, with 2 of them having a systematic code orange, totaling 43 (2%) fractions with at least one code orange. The IGRT-RTT assigned code orange or red in 59 (94%) patients, 38 (60%) of which had systematic codes orange. In total, the IGRT-RTT reported 684 (40%) fractions with code orange and 13 with code red. During TLP2019, similar numbers are observed. In the subset reviewed by two IGRT-RTTs, reports matched in 77% of cases. Various factors contribute to a low reporting rate, originating both during the decision-making process such as lack of online reporting tools and within offline processes such as divergent feedback expectations.

Conclusion

While our TLP has successfully promoted the widespread adoption of CBCT-based RTT-led IGRT, it has not succeeded in establishing interdisciplinary communication. Our study reveals significant underreporting of flagged LA-NSCLC fractions in clinical practice using a TLP. This underreporting stems from multifactorial origins.

CBCT-based online adaptive radiotherapy of the prostate bed: first clinical experience and comparison to nonadaptive conventional IGRT

PURPOSE

Conventional image-guided radiotherapy (IGRT) of the prostate bed is challenged by the varying anatomy due to dynamic changes of surrounding organs such as the bladder and rectum. This leads to changed dose coverage of target and surrounding tissue. The novel online adaptive radiotherapy (oART) aims to improve target coverage as well as reduce dose exposure to surrounding healthy tissues by daily reoptimization of treatment plans. Here we set out to quantify the resulting changes of this adaptation for patients and treatment team.

METHODS

A total of 198 fractions of radiotherapy of the prostate bed (6 patients) were treated using oART with the Ethos accelerator (Varian Medical Systems, Palo Alto, CA, USA). For each fraction, volumes and several dose-volume parameters of target volumes and organs at risk were recorded for the scheduled plan (initial plan, recalculated based on daily cone beam computed tomography [CBCT]), the adapted plan, and the verification plan, which is the dose distribution of the applied plan recalculated on the closing CBCT after the adaptation process. Clinical acceptability for all plans was determined using given dose-volume parameters of target volumes. Additionally, the time needed for the adaptation process was registered and compared to the time required for the daily treatment of five conventional IGRT patients.

RESULTS

Volumes of target and organs at risk (OAR) exhibited broad variation from day to day. The differences in dose coverage D98% of the clinical target volume (CTV) were significant through adaptation (p < 0.0001; median D98% 97.1-98.0%) and further after verification CBCT (p < 0.001; median D98% 98.1%). Similarly, differences in D98% of the planning target volume (PTV) were significant with adaptation (p < 0.0001; median D98% 91.8-96.5%) and after verification CBCT (p < 0.001; median D98% 96.4%) with decreasing interquartile ranges (IQR). Dose to OAR varied extensively and did not show a consistent benefit from oART but decreased in IQR. Clinical acceptability increased significantly from 19.2% for scheduled plans to 76.8% for adapted plans and decreased to 70.7% for verification plans. The scheduled plan was never chosen for treatment. The median time needed for oART was 25 min compared to 8 min for IGRT.

CONCLUSION

Target dose coverage was significantly improved using oART. IQR decreased for target coverage as well as OAR doses indicating higher repeatability of dose delivery using oART. Differences in doses after verification CBCT for targets as well as OAR were significant compared to adapted plans but did not offset the overall dosimetric gain of oART. The median time required is three times higher for oART compared to IGRT.

Current state, challenges, and future perspective of adaptive radiotherapy: A narrative review of nasopharyngeal carcinoma

Patients with nasopharyngeal carcinoma often experience weight loss and tumor regression during the course of radiotherapy that lasts for up to 6-7 weeks. Adaptive radiotherapy is a systematic feedback control approach based on image-guided technology that adjusts these changes and optimizes the radiotherapy plans according to new imaging findings during treatment. There is growing evidence that adaptive radiotherapy can reduce side effects, improve the quality of life, and enhance disease control. However, the routine application of adaptive radiotherapy for nasopharyngeal remains relatively limited. This review discusses the necessity, clinical benefits, and limitations of adaptive radiotherapy, and presents the current state, challenges, and future perspective of adaptive radiotherapy strategies for nasopharyngeal carcinoma.

Adaptive Approach to Treating Cervical Cancer in a Patient With Dramatic Uterine Movement

Adaptive radiation therapy is a modern technological advancement that allows radiation treatments to be adjusted daily to account for changes in the patient's anatomy, such as bladder and rectal filling, as well as changes in the tumor volume and position. In this case report, we present a patient with locally advanced cervical cancer who received definitive radiation therapy of 4500 cGy in 25 fractions using the Varian's Ethos system. We observed substantial daily uterine movement, which required re-optimization of each treatment fraction. Without the daily plan adaptation, the treatment would have resulted in markedly suboptimal dose coverage to the tumor. This case report highlights the importance of adaptive radiotherapy in managing anatomical changes in cervical cancer treatment and improving outcomes.

Prior-image-based low-dose CT reconstruction for adaptive radiation therapy

Objective. The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring.Approach. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region.Main results. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure.Significance. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.

Adaptive assessment based on fractional CBCT images for cervical cancer

PURPOSE

Anatomical and other changes during radiotherapy will cause inaccuracy of dose distributions, therefore the expectation for online adaptive radiation therapy (ART) is high in effectively reducing uncertainties due to intra-variation. However, ART requires extensive time and effort. This study investigated an adaptive assessment workflow based on fractional cone-beam computed tomography (CBCT) images.

METHODS

Image registration, synthetic CT (sCT) generation, auto-segmentation, and dose calculation were implemented and integrated into ArcherQA Adaptive Check. The rigid registration was based on ITK open source. The deformable image registration (DIR) method was based on a 3D multistage registration network, and the sCT generation method was performed based on a 2D cycle-consistent adversarial network (CycleGAN). The auto-segmentation of organs at risk (OARs) on sCT images was finished by a deep learning-based auto-segmentation software, DeepViewer. The contours of targets were obtained by the structure-guided registration. Finally, the dose calculation was based on a GPU-based Monte Carlo (MC) dose code, ArcherQA.

RESULTS

The dice similarity coefficient (DSCs) were over 0.86 for target volumes and over 0.79 for OARs. The gamma pass rate of ArcherQA versus Eclipse treatment planning system was more than 99% at the 2%/2 mm criterion with a low-dose threshold of 10%. The time for the whole process was less than 3 min. The dosimetric results of ArcherQA Adaptive Check were consistent with the Ethos scheduled plan, which can effectively identify the fractions that need the implementation of the Ethos adaptive plan.

CONCLUSION

This study integrated AI-based technologies and GPU-based MC technology to evaluate the dose distributions using fractional CBCT images, demonstrating remarkably high efficiency and precision to support future ART processes.

Comparison of online adaptive and non-adaptive magnetic resonance image-guided radiation therapy in prostate cancer using dose accumulation

Background and purpose

Conventional image-guided radiotherapy (conv-IGRT) is standard in prostate cancer (PC) but does not account for inter-fraction anatomical changes. Online-adaptive magnetic resonance-guided RT (OA-MRgRT) may improve organ-at-risk (OARs) sparing and clinical target volume (CTV) coverage. The aim of this study was to analyze accumulated OAR and target doses in PC after OA-MRgRT and conv-IGRT in comparison to pre-treatment reference planning (refPlan).

Material and methods

Ten patients with PC, previously treated with OA-MRgRT at the 1.5 T MR-Linac (20x3Gy), were included. Accumulated OA-MRgRT doses were determined by deformably registering all fraction's MR-images. Conv-IGRT was simulated through rigid registration of the planning computed tomography with each fraction's MR-image for dose mapping/accumulation. Dose-volume parameters (DVPs), including CTV D50% and D98%, rectum, bladder, urethra, Dmax and V56Gy for OA-MRgRT, conv-IGRT and refPlan were compared using the Wilcoxon signed-rank test. Clinical relevance of accumulated dose differences was analyzed using a normal-tissue complication-probability model.

Results

CTV-DVPs were comparable, whereas OA-MRgRT yielded decreased median OAR-DVPs compared to conv-IGRT, except for bladder V56Gy. OA-MRgRT demonstrated significantly lower median rectum Dmax over conv-IGRT (59.1/59.9 Gy, p = 0.006) and refPlan (60.1 Gy, p = 0.012). Similarly, OA-MRgRT yielded reduced median bladder Dmax compared to conv-IGRT (60.0/60.4 Gy, p = 0.006), and refPlan (61.2 Gy, p = 0.002). Overall, accumulated dose differences were small and did not translate into clinically relevant effects.

Conclusion

Deformably accumulated OA-MRgRT using 20x3Gy in PC showed significant but small dosimetric differences comparted to conv-IGRT. Feasibility of a dose accumulation methodology was demonstrated, which may be relevant for evaluating future hypo-fractionated OA-MRgRT approaches.

Adapt-On-Demand: A Novel Strategy for Personalized Adaptive Radiation Therapy for Locally Advanced Lung Cancer

PURPOSE

Real-time adaptation of thoracic radiation plans is compelling because offline adaptive experiences show that tumor volumes and lung anatomy can change during therapy. We present and analyze a novel adaptive-on-demand (AOD) workflow combining online adaptive radiation therapy (o-ART) on the ETHOS system with image guided radiation therapy delivery on a Halcyon unit for conventional fractionated radiation therapy of locally advanced lung cancer (LALC).

METHODS AND MATERIALS

We analyzed 26 patients with LALC treated with the AOD workflow, adapting weekly. We timed segments of the workflow to evaluate efficiency in a real-world clinic. Target coverage and organ at risk (OAR) doses were compared between adaptive plans (ADP) and nonadaptive scheduled plans (SCH). Planning robustness was evaluated by the frequency of preplanning goals achieved in ADP plans, stratified by tumor volume change.

RESULTS

The AOD workflow was achievable within 30 minutes for most radiation fractions. Over the course of therapy, we observed an average 26.6% ± 23.3% reduction in internal target volume (ITV). Despite these changes, with o-ART, ITV and planning target volume (PTV) coverage (V100%) was 99.2% and 93.9% for all members of the cohort, respectively. This represented a 2.9% and 6.8% improvement over nonadaptive plans (P < .05), respectively. For tumors that grew >10%, V100% was 93.1% for o-ART and 76.4% for nonadaptive plans, representing a median 17.2% improvement in the PTV coverage (P < .05). In these plans, critical OAR constraints were met 94.1% of the time, whereas in nonadaptive plans, this figure was 81.9%. This represented reductions of 1.32 Gy, 1.34 Gy, or 1.75 Gy in the heart, esophagus, and lung, respectively. The effect was larger when tumors had shrunk more than 10%. Regardless of tumor volume alterations, the PTV/ITV coverage was achieved for all adaptive plans. Exceptional cases, where dose constraints were not met, were due to large initial tumor volumes or tumor growth.

CONCLUSIONS

The AOD workflow is efficient and robust in responding to anatomic changes in LALC patients, providing dosimetric advantages over standard therapy. Weekly adaptation was adequate to keep pace with changes. This approach is a feasible alternative to conventional offline replanning workflows for managing anatomy changes in LALC radiation therapy.

Robust optimization strategies for contour uncertainties in online adaptive radiation therapy

Objective.Online adaptive radiation therapy requires fast and automated contouring of daily scans for treatment plan re-optimization. However, automated contouring is imperfect and introduces contour uncertainties. This work aims at developing and comparing robust optimization strategies accounting for such uncertainties.Approach.A deep-learning method was used to predict the uncertainty of deformable image registration, and to generate a finite set of daily contour samples. Ten optimization strategies were compared: two baseline methods, five methods that convert contour samples into voxel-wise probabilities, and three methods accounting explicitly for contour samples as scenarios in robust optimization. Target coverage and organ-at-risk (OAR) sparing were evaluated robustly for simplified proton therapy plans for five head-and-neck cancer patients.Results.We found that explicitly including target contour uncertainty in robust optimization provides robust target coverage with better OAR sparing than the baseline methods, without increasing the optimization time. Although OAR doses first increased when increasing target robustness, this effect could be prevented by additionally including robustness to OAR contour uncertainty. Compared to the probability-based methods, the scenario-based methods spared the OARs more, but increased integral dose and required more computation time.Significance.This work proposed efficient and beneficial strategies to mitigate contour uncertainty in treatment plan optimization. This facilitates the adoption of automatic contouring in online adaptive radiation therapy and, more generally, enables mitigation also of other sources of contour uncertainty in treatment planning.

Enhancing adaptive proton therapy through CBCT images: Synthetic head and neck CT generation based on 3D vision transformers

BACKGROUND

Proton therapy is a form of radiotherapy commonly used to treat various cancers. Due to its high conformality, minor variations in patient anatomy can lead to significant alterations in dose distribution, making adaptation crucial. While cone-beam computed tomography (CBCT) is a well-established technique for adaptive radiation therapy (ART), it cannot be directly used for adaptive proton therapy (APT) treatments because the stopping power ratio (SPR) cannot be estimated from CBCT images.

PURPOSE

To address this limitation, Deep Learning methods have been suggested for converting pseudo-CT (pCT) images from CBCT images. In spite of convolutional neural networks (CNNs) have shown consistent improvement in pCT literature, there is still a need for further enhancements to make them suitable for clinical applications.

METHODS

The authors introduce the 3D vision transformer (ViT) block, studying its performance at various stages of the proposed architectures. Additionally, they conduct a retrospective analysis of a dataset that includes 259 image pairs from 59 patients who underwent treatment for head and neck cancer. The dataset is partitioned into 80% for training, 10% for validation, and 10% for testing purposes.

RESULTS

The SPR maps obtained from the pCT using the proposed method present an absolute relative error of less than 5% from those computed from the planning CT, thus improving the results of CBCT.

CONCLUSIONS

We introduce an enhanced ViT3D architecture for pCT image generation from CBCT images, reducing SPR error within clinical margins for APT workflows. The new method minimizes bias compared to CT-based SPR estimation and dose calculation, signaling a promising direction for future research in this field. However, further research is needed to assess the robustness and generalizability across different medical imaging applications.

Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy

Background and purpose

Accurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision.

Materials and methods

We introduce a novel framework that incorporates data from a patient's initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current fraction's CBCT images. We present LSTM-UNet, an innovative architecture that integrates Long Short-Term Memory (LSTM) units into the skip connections of the traditional U-Net framework to retain information from previous fractions. The models underwent initial pre-training with simulated data followed by fine-tuning on a clinical dataset.

Results

Our proposed model's segmentation predictions yield an average Dice similarity coefficient of 79% from 8 Head & Neck organs and targets, compared to 52% from a baseline model without prior knowledge and 78% from a baseline model with prior knowledge but no memory.

Conclusions

Our proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with registration-based methods that offer better prior knowledge. Our model holds promise for integration into the online ART workflow, offering precise segmentation capabilities on synthetic CT images.

A reference-point-method-based online proton treatment plan re-optimization strategy and a novel solution to planning constraint infeasibility problem

Objective. Propose a highly automated treatment plan re-optimization strategy suitable for online adaptive proton therapy. The strategy includes a rapid re-optimization method that generates quality replans and a novel solution that efficiently addresses the planning constraint infeasibility issue that can significantly prolong the re-optimization process.Approach. We propose a systematic reference point method (RPM) model that minimizes the l-infinity norm from the initial treatment plan in the daily objective space for online re-optimization. This model minimizes the largest objective value deviation among the objectives of the daily replan from their reference values, leading to a daily replan similar to the initial plan. Whether a set of planning constraints is feasible with respect to the daily anatomy cannot be known before solving the corresponding optimization problem. The conventional trial-and-error-based relaxation process can cost a significant amount of time. To that end, we propose an optimization problem that first estimates the magnitude of daily violation of each planning constraint. Guided by the violation magnitude and clinical importance of the constraints, the constraints are then iteratively converted into objectives based on their priority until the infeasibility issue is solved.Main results.The proposed RPM-based strategy generated replans similar to the offline manual replans within the online time requirement for six head and neck and four breast patients. The average targetD95and relevant organ at risk sparing parameter differences between the RPM replans and clinical offline replans were -0.23, -1.62 Gy for head and neck cases and 0.29, -0.39 Gy for breast cases. The proposed constraint relaxation solution made the RPM problem feasible after one round of relaxation for all four patients who encountered the infeasibility issue.Significance. We proposed a novel RPM-based re-optimization strategy and demonstrated its effectiveness on complex cases, regardless of whether constraint infeasibility is encountered.

Assessment of the deep learning-based gamma passing rate prediction system for 1.5 T magnetic resonance-guided linear accelerator

Measurement-based verification is impossible for the patient-specific quality assurance (QA) of online adaptive magnetic resonance imaging-guided radiotherapy (oMRgRT) because the patient remains on the couch throughout the session. We assessed a deep learning (DL) system for oMRgRT to predict the gamma passing rate (GPR). This study collected 125 verification plans [reference plan (RP), 100; adapted plan (AP), 25] from patients with prostate cancer treated using Elekta Unity. Based on our previous study, we employed a convolutional neural network that predicted the GPRs of nine pairs of gamma criteria from 1%/1 mm to 3%/3 mm. First, we trained and tested the DL model using RPs (n = 75 and n = 25 for training and testing, respectively) for its optimization. Second, we tested the GPR prediction accuracy using APs to determine whether the DL model could be applied to APs. The mean absolute error (MAE) and correlation coefficient (r) of the RPs were 1.22 ± 0.27% and 0.29 ± 0.10 in 3%/2 mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2 mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1 mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2 mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2 mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1 mm, respectively. The time cost was within 3 s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

Artificial Intelligence and the future of radiotherapy planning: The Australian radiation therapists prepare to be ready

The use of artificial intelligence (AI) solutions is rapidly changing the way radiation therapy tasks, traditionally relying on human skills, are approached by enabling fast automation. This evolution represents a paradigm shift in all aspects of the profession, particularly for treatment planning applications, opening up opportunities but also causing concerns for the future of the multidisciplinary team. In Australia, radiation therapists (RTs), largely responsible for both treatment planning and delivery, are discussing the impact of the introduction of AI and the potential developments in the future of their role. As medical physicists, who are part of the multidisciplinary team, in this editorial we reflect on the considerations of RTs, and on the implications of this transition to AI.

A prospective observational study evaluating two patient immobilisation methods in lung stereotactic radiotherapy

PURPOSE

The main objective of this study was to assess inter- and intrafraction errors for two patient immobilisation devices in the context of lung stereotactic body radiation therapy: a vacuum cushion and a simple arm support.

MATERIALS AND METHODS

Twenty patients who were treated with lung stereotactic body radiation therapy in supine position with arms above their head were included in the study. Ten patients were setup in a vacuum cushion (Bluebag™, Elekta) and ten other patients with a simple arm support (Posirest™, Civco). A pretreatment four-dimensional cone-beam computed tomography and a post-treatment three-dimensional cone-beam computed tomography were acquired to compare positioning and immobilisation accuracy. Based on a rigid registration with the planning computed tomography on the spine at the target level, translational and rotational errors were reported.

RESULTS

The median number of fractions per treatment was 5 (range: 3-10). Mean interfraction errors based on 112 four-dimensional cone-beam computed tomographies were similar for both setups with deviations less than or equal to 1.3mm in lateral and vertical direction and 1.2° in roll and yaw. For longitudinal translational errors, mean interfraction errors were 0.7mm with vacuum cushion and -3.9mm with arm support. Based on 111 three-dimensional cone-beam computed tomographies, mean lateral, longitudinal and vertical intrafraction errors were -0.1mm, -0.2mm and 0.0mm respectively (SD: 1.0, 1.2 and 1.0mm respectively) for the patients setup with vacuum cushion, and mean vertical, longitudinal and lateral intrafraction errors were -0.3mm, -0.7mm and 0.1mm respectively (SD: 2.3, 1.8 and 1.4mm respectively) for the patients setup with arm support. Intrafraction errors means were not statistically different between both positions but standard deviations were statistically larger with arm support.

CONCLUSION

The results of our study showed similar inter and intrafraction mean deviations between both positioning but a large variability in intrafraction observed with arm support suggested a more accurate immobilization with vacuum cushion.

Unlocking the adaptive advantage: correlation and machine learning classification to identify optimal online adaptive stereotactic partial breast candidates

Objective.Online adaptive radiotherapy (OART) is a promising technique for delivering stereotactic accelerated partial breast irradiation (APBI), as lumpectomy cavities vary in location and size between simulation and treatment. However, OART is resource-intensive, increasing planning and treatment times and decreasing machine throughput compared to the standard of care (SOC). Thus, it is pertinent to identify high-yield OART candidates to best allocate resources.Approach.Reference plans (plans based on simulation anatomy), SOC plans (reference plans recalculated onto daily anatomy), and daily adaptive plans were analyzed for 31 sequential APBI targets, resulting in the analysis of 333 treatment plans. Spearman correlations between 22 reference plan metrics and 10 adaptive benefits, defined as the difference between mean SOC and delivered metrics, were analyzed to select a univariate predictor of OART benefit. A multivariate logistic regression model was then trained to stratify high- and low-benefit candidates.Main results.Adaptively delivered plans showed dosimetric benefit as compared to SOC plans for most plan metrics, although the degree of adaptive benefit varied per patient. The univariate model showed high likelihood for dosimetric adaptive benefit when the reference plan ipsilateral breast V15Gy exceeds 23.5%. Recursive feature elimination identified 5 metrics that predict high-dosimetric-benefit adaptive patients. Using leave-one-out cross validation, the univariate and multivariate models classified targets with 74.2% and 83.9% accuracy, resulting in improvement in per-fraction adaptive benefit between targets identified as high- and low-yield for 7/10 and 8/10 plan metrics, respectively.Significance.This retrospective, exploratory study demonstrated that dosimetric benefit can be predicted using only ipsilateral breast V15Gy on the reference treatment plan, allowing for a simple, interpretable model. Using multivariate logistic regression for adaptive benefit prediction led to increased accuracy at the cost of a more complicated model. This work presents a methodology for clinics wishing to triage OART resource allocation.

A prospective study on the effect of tumor shrinkage on exit fluence gamma pass rate in high precision radiotherapy and influence of phantom setup error in patient-specific quality assurance

PURPOSE

Objective parameters for decision on adaptive radiotherapy depend on patient, tumor and treatment related factors. Present study reports geometric uncertainties occurring during high precision radiotherapy, beam fluence analysis and serial exit dose measurement as a patient-specific tool for adaptive radiotherapy.

MATERIALS AND METHODS

Serial exit dose fluence of 24 patients (at baseline and mid-treatment) undergoing IMRT/VMAT treatment were measured. Baseline and midtreatment exit dose evaluation was done using gafchromic films in predefined region of interest. Difference of volume of GTV at baseline (from simulation CT scan) and midtreatment CBCT scan was calculated (ΔGTV).

RESULTS

Population based systematic errors (mm) were 4.15, 2.26, 0.88 and random errors (mm) were 2.56, 3.69, and 2.03 in mediolateral (ML), craniocaudal (CC) and anteroposterior (AP) directions respectively. Gamma pass rate reduced with incremental shift. For a 5 mm shift, maximum deviation was found in anteroposterior axis (22.16 ± 7.50) and lowest in mediolateral axis (12.85 ± 4.95). On serial measurement of exit dose fluence, tumor shrinkage significantly influenced gamma pass rate. The mean gamma pass rate was significantly different between groups with 50% shrinkage of tumor volume (86.36 vs 96.24, P = 0.008, on multivariate analysis P = 0.026).

CONCLUSION

Rapid fall of gamma pass rate was observed for set up error of ≥3 mm. Serial measurement of exit dose fluence by radiochromic film is a feasible method of exit dose comparison in IMRT/VMAT, where EPID dosimetry is not available with linear accelerator configuration. Our study suggests that there is a significant difference between gamma pass rates of baseline and mid treatment exit dose fluence with greater than 50% tumor shrinkage.

Clinical Workflow of Cone Beam Computer Tomography-Based Daily Online Adaptive Radiotherapy with Offline Magnetic Resonance Guidance: The Modular Adaptive Radiotherapy System (MARS)

PURPOSE

The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present the clinical workflow of CBCT-based oART with shuttle-based offline MR guidance.

METHODS

From February to November 2023, 31 patients underwent radiotherapy on the Ethos (Varian, Palo Alto, CA, USA) system with machine learning (ML)-supported daily oART. Moreover, patients received weekly MRI in treatment position, which was utilized for daily plan adaptation, via a shuttle-based system. Initial and adapted treatment plans were generated using the Ethos treatment planning system. Patient clinical data, fractional session times (MRI + shuttle transport + positioning, adaptation, QA, RT delivery) and plan selection were assessed for all fractions in all patients.

RESULTS

In total, 737 oART fractions were applied and 118 MRIs for offline MR guidance were acquired. Primary sites of tumors were prostate (n = 16), lung (n = 7), cervix (n = 5), bladder (n = 1) and endometrium (n = 2). The treatment was completed in all patients. The median MRI acquisition time including shuttle transport and positioning to initiation of the Ethos adaptive session was 53.6 min (IQR 46.5-63.4). The median total treatment time without MRI was 30.7 min (IQR 24.7-39.2). Separately, median adaptation, plan QA and RT times were 24.3 min (IQR 18.6-32.2), 0.4 min (IQR 0.3-1,0) and 5.3 min (IQR 4.5-6.7), respectively. The adapted plan was chosen over the scheduled plan in 97.7% of cases.

CONCLUSION

This study describes the first workflow to date of a CBCT-based oART combined with a shuttle-based offline approach for MR guidance. The oART duration times reported resemble the range shown by previous publications for first clinical experiences with the Ethos system.

Synthetic CT imaging for PET monitoring in proton therapy: a simulation study

Objective.This study addresses a fundamental limitation of in-beam positron emission tomography (IB-PET) in proton therapy: the lack of direct anatomical representation in the images it produces. We aim to overcome this shortcoming by pioneering the application of deep learning techniques to create synthetic control CT images (sCT) from combining IB-PET and planning CT scan data.Approach.We conducted simulations involving six patients who underwent irradiation with proton beams. Leveraging the architecture of a visual transformer (ViT) neural network, we developed a model to generate sCT images of these patients using the planning CT scans and the inter-fractional simulated PET activity maps during irradiation. To evaluate the model's performance, a comparison was conducted between the sCT images produced by the ViT model and the authentic control CT images-serving as the benchmark.Main results.The structural similarity index was computed at a mean value across all patients of 0.91, while the mean absolute error measured 22 Hounsfield Units (HU). Root mean squared error and peak signal-to-noise ratio values were 56 HU and 30 dB, respectively. The Dice similarity coefficient exhibited a value of 0.98. These values are comparable to or exceed those found in the literature. More than 70% of the synthetic morphological changes were found to be geometrically compatible with the ones reported in the real control CT scan.Significance.Our study presents an innovative approach to surface the hidden anatomical information of IB-PET in proton therapy. Our ViT-based model successfully generates sCT images from inter-fractional PET data and planning CT scans. Our model's performance stands on par with existing models relying on input from cone beam CT or magnetic resonance imaging, which contain more anatomical information than activity maps.

Improved Dosimetry and Plan Quality for Accelerated Partial Breast Irradiation Using Online Adaptive Radiation Therapy: A Single Institutional Study

Purpose

Accelerated partial breast irradiation (APBI) is an attractive treatment modality for eligible patients as it has been shown to result in similar local control and improved cosmetic outcomes compared with whole breast radiation therapy. The use of online adaptive radiation therapy (OART) for APBI is promising as it allows for a reduction of planning target volume margins because breast motion and lumpectomy cavity volume changes are accounted for in daily imaging. Here we present a retrospective, single-institution evaluation on the adequacy of kV-cone beam computed tomography (CBCT) OART for APBI treatments.

Methods and Materials

Nineteen patients (21 treatment sites) were treated to 30 Gy in 5 fractions between January of 2022 and May of 2023. Time between simulation and treatment, change in gross tumor (ie, lumpectomy cavity) volume, and differences in dose volume histogram metrics with adaption were analyzed. The Wilcoxon paired, nonparametric test was used to test for dose volume histogram metric differences between the scheduled plans (initial plans recalculated on daily CBCT anatomy) and delivered plans, either the scheduled or adapted plan, which was reoptimized using daily anatomy.

Results

Median (interquartile range) time from simulation to first treatment was 26 days (21-32 days). During this same time, median gross tumor volume reduction was 16.0% (7.3%-23.9%) relative to simulation volume. Adaptive treatments took 31.3 minutes (27.4-36.6 minutes) from start of CBCT to treatment session end. At treatment, the adaptive plan was selected for 86% (89/103) of evaluable fractions. In evaluating plan quality, 78% of delivered plans met all target, organs at risk, and conformity metrics evaluated, compared with 34% of scheduled plans.

Conclusions

Use of OART for stereotactic linac-based APBI allowed for safe, high-quality treatments in this cohort of 21 treatment courses. Although treatment delivery times were longer than traditional stereotactic body treatments, there were notable improvements in plan quality for APBI using OART.

Daily AI-Based Treatment Adaptation under Weekly Offline MR Guidance in Chemoradiotherapy for Cervical Cancer 1: The AIM-C1 Trial

(1) Background: External beam radiotherapy (EBRT) and concurrent chemotherapy, followed by brachytherapy (BT), offer a standard of care for patients with locally advanced cervical carcinoma. Conventionally, large safety margins are required to compensate for organ movement, potentially increasing toxicity. Lately, daily high-quality cone beam CT (CBCT)-guided adaptive radiotherapy, aided by artificial intelligence (AI), became clinically available. Thus, online treatment plans can be adapted to the current position of the tumor and the adjacent organs at risk (OAR), while the patient is lying on the treatment couch. We sought to evaluate the potential of this new technology, including a weekly shuttle-based 3T-MRI scan in various treatment positions for tumor evaluation and for decreasing treatment-related side effects. (2) Methods: This is a prospective one-armed phase-II trial consisting of 40 patients with cervical carcinoma (FIGO IB-IIIC1) with an age ≥ 18 years and a Karnofsky performance score ≥ 70%. EBRT (45-50.4 Gy in 25-28 fractions with 55.0-58.8 Gy simultaneous integrated boosts to lymph node metastases) will be accompanied by weekly shuttle-based MRIs. Concurrent platinum-based chemotherapy will be given, followed by 28 Gy of BT (four fractions). The primary endpoint will be the occurrence of overall early bowel and bladder toxicity CTCAE grade 2 or higher (CTCAE v5.0). Secondary outcomes include clinical feasibility, quality of life, and imaging-based response assessment.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy

BACKGROUND

Accurate and efficient dose calculation is essential for on-line adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results in photon therapy. However, there is a scarcity of DL-based dose prediction methods specifically designed for proton therapy. Successful dose prediction method for proton therapy should account for more challenging dose prediction problems in pencil beam scanning proton therapy (PBSPT) due to its sensitivity to heterogeneities.

PURPOSE

To develop a DL-based PBSPT dose prediction workflow with high accuracy and balanced complexity to support on-line adaptive proton therapy clinical decision and subsequent replanning.

METHODS

PBSPT plans of 103 prostate cancer patients (93 for training and the other 10 for independent testing) and 83 lung cancer patients (73 for training and the other 10 for independent testing) previously treated at our institution were included in the study, each with computed tomography scans (CTs), structure sets, and plan doses calculated by the in-house developed Monte-Carlo dose engine (considered as the ground truth in the model training and testing). For the ablation study, we designed three experiments corresponding to the following three methods: (1) Experiment 1, the conventional region of interest (ROI) (composed of targets and organs-at-risk [OARs]) method. (2) Experiment 2, the beam mask (generated by raytracing of proton beams) method to improve proton dose prediction. (3) Experiment 3, the sliding window method for the model to focus on local details to further improve proton dose prediction. A fully connected 3D-Unet was adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing rates with a criterion of 3%/3 mm/10%, and dice coefficients for the structures enclosed by the iso-dose lines between the predicted and the ground truth doses were used as the evaluation metrics. The calculation time for each proton dose prediction was recorded to evaluate the method's efficiency.

RESULTS

Compared to the conventional ROI method, the beam mask method improved the agreement of DVH indices for both targets and OARs and the sliding window method further improved the agreement of the DVH indices (for lung cancer, CTV D98 absolute deviation: 0.74 ± 0.18 vs. 0.57 ± 0.21 vs. 0.54 ± 0.15 Gy[RBE], ROI vs. beam mask vs. sliding window methods, respectively). For the 3D Gamma passing rates in the target, OARs, and BODY (outside target and OARs), the beam mask method improved the passing rates in these regions and the sliding window method further improved them (for prostate cancer, targets: 96.93% ± 0.53% vs. 98.88% ± 0.49% vs. 99.97% ± 0.07%, BODY: 86.88% ± 0.74% vs. 93.21% ± 0.56% vs. 95.17% ± 0.59%). A similar trend was also observed for the dice coefficients. This trend was especially remarkable for relatively low prescription isodose lines (for lung cancer, 10% isodose line dice: 0.871 ± 0.027 vs. 0.911 ± 0.023 vs. 0.927 ± 0.017). The dose predictions for all the testing cases were completed within 0.25 s.

CONCLUSIONS

An accurate and efficient deep learning-augmented proton dose prediction framework has been developed for PBSPT, which can predict accurate dose distributions not only inside but also outside ROI efficiently. The framework can potentially further reduce the initial planning and adaptive replanning workload in PBSPT.

A review of the clinical introduction of 4D particle therapy research concepts

Background and purpose

Many 4D particle therapy research concepts have been recently translated into clinics, however, remaining substantial differences depend on the indication and institute-related aspects. This work aims to summarise current state-of-the-art 4D particle therapy technology and outline a roadmap for future research and developments.

Material and methods

This review focused on the clinical implementation of 4D approaches for imaging, treatment planning, delivery and evaluation based on the 2021 and 2022 4D Treatment Workshops for Particle Therapy as well as a review of the most recent surveys, guidelines and scientific papers dedicated to this topic.

Results

Available technological capabilities for motion surveillance and compensation determined the course of each 4D particle treatment. 4D motion management, delivery techniques and strategies including imaging were diverse and depended on many factors. These included aspects of motion amplitude, tumour location, as well as accelerator technology driving the necessity of centre-specific dosimetric validation. Novel methodologies for X-ray based image processing and MRI for real-time tumour tracking and motion management were shown to have a large potential for online and offline adaptation schemes compensating for potential anatomical changes over the treatment course. The latest research developments were dominated by particle imaging, artificial intelligence methods and FLASH adding another level of complexity but also opportunities in the context of 4D treatments.

Conclusion

This review showed that the rapid technological advances in radiation oncology together with the available intrafractional motion management and adaptive strategies paved the way towards clinical implementation.

Safety and Efficacy of Neoadjuvant SABR in Pancreatic Cancer: Effect of Magnetic Resonance Imaging-Guided Respiratory-Gated Adaptive Radiation Therapy

Purpose

We aimed to evaluate the safety and efficacy of neoadjuvant SABR using magnetic resonance imaging-guided respiratory-gated adaptive radiation therapy (MRgRg-ART) in pancreatic cancer.

Methods and Materials

We performed a single-institution retrospective review in patients with pancreatic cancer who underwent neoadjuvant SABR followed by surgical resection. After neoadjuvant chemotherapy, those considered resectable by the multidisciplinary team received SABR over 5 consecutive days using MRgRg-ART. Factors associated with severe postoperative complications (Clavien-Dindo grade ≥III) and prognostic factors for overall survival were analyzed.

Results

Sixty-two patients were included in the analysis, with a median follow-up of 10.3 months. The median prescribed dose to the planning target volume was 50 Gy. Fifty-two (85.3%) patients underwent R0 resection, and 11 (18.0%) experienced severe postoperative complications. No factors were associated with the incidence of severe postoperative complications. There were 3 cases of locoregional recurrence, resulting in a 12-month local control rate of 93.1%. Elevated postoperative carbohydrate antigen 19-9 was significantly associated with poor overall survival in the multivariate analysis (P = .037).

Conclusions

Neoadjuvant SABR with 50 Gy using MRgRg-ART delivered to pancreatic cancer resulted in a notable survival outcome with acceptable toxicities. Further studies are warranted to investigate the long-term effects of this method.

Quantifying the Effect of 4-Dimensional Computed Tomography-Based Deformable Dose Accumulation on Representing Radiation Damage for Patients with Locally Advanced Non-Small Cell Lung Cancer Treated with Standard-Fractionated Intensity-Modulated Radiation Therapy

PURPOSE

The aim of this study was to investigate the dosimetric and clinical effects of 4-dimensional computed tomography (4DCT)-based longitudinal dose accumulation in patients with locally advanced non-small cell lung cancer treated with standard-fractionated intensity-modulated radiation therapy (IMRT).

METHODS AND MATERIALS

Sixty-seven patients were retrospectively selected from a randomized clinical trial. Their original IMRT plan, planning and verification 4DCTs, and ∼4-month posttreatment follow-up CTs were imported into a commercial treatment planning system. Two deformable image registration algorithms were implemented for dose accumulation, and their accuracies were assessed. The planned and accumulated doses computed using average-intensity images or phase images were compared. At the organ level, mean lung dose and normal-tissue complication probability (NTCP) for grade ≥2 radiation pneumonitis were compared. At the region level, mean dose in lung subsections and the volumetric overlap between isodose intervals were compared. At the voxel level, the accuracy in estimating the delivered dose was compared by evaluating the fit of a dose versus radiographic image density change (IDC) model. The dose-IDC model fit was also compared for subcohorts based on the magnitude of NTCP difference (|ΔNTCP|) between planned and accumulated doses.

RESULTS

Deformable image registration accuracy was quantified, and the uncertainty was considered for the voxel-level analysis. Compared with planned doses, accumulated doses on average resulted in <1-Gy lung dose increase and <2% NTCP increase (up to 8.2 Gy and 18.8% for a patient, respectively). Volumetric overlap of isodose intervals between the planned and accumulated dose distributions ranged from 0.01 to 0.93. Voxel-level dose-IDC models demonstrated a fit improvement from planned dose to accumulated dose (pseudo-R2 increased 0.0023) and a further improvement for patients with ≥2% |ΔNTCP| versus for patients with <2% |ΔNTCP|.

CONCLUSIONS

With a relatively large cohort, robust image registrations, multilevel metric comparisons, and radiographic image-based evidence, we demonstrated that dose accumulation more accurately represents the delivered dose and can be especially beneficial for patients with greater longitudinal response.

The Use of MR-Guided Radiation Therapy for Head and Neck Cancer and Recommended Reporting Guidance

Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as "biological adaptation" based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.

Review and recommendations on deformable image registration uncertainties for radiotherapy applications

Deformable image registration (DIR) is a versatile tool used in many applications in radiotherapy (RT). DIR algorithms have been implemented in many commercial treatment planning systems providing accessible and easy-to-use solutions. However, the geometric uncertainty of DIR can be large and difficult to quantify, resulting in barriers to clinical practice. Currently, there is no agreement in the RT community on how to quantify these uncertainties and determine thresholds that distinguish a good DIR result from a poor one. This review summarises the current literature on sources of DIR uncertainties and their impact on RT applications. Recommendations are provided on how to handle these uncertainties for patient-specific use, commissioning, and research. Recommendations are also provided for developers and vendors to help users to understand DIR uncertainties and make the application of DIR in RT safer and more reliable.

Optimising the MR-Linac as a standard treatment modality

The magnetic resonance linear accelerator (MR-Linac) offers a new treatment paradigm, providing improved visualisation of targets and organs at risk while allowing for daily adaptation of treatment plans in real time. Online MR-guided adaptive treatment has reduced treatment uncertainties; however, the additional treatment time and resource requirements may be a concern. We present our experience of integrating an MR-Linac into a busy department and provide recommendations for improved clinical and resource efficiency. Furthermore, we discuss potential future technological innovations that can further optimise clinical productivity in a busy department.