Challenges and opportunities in the development and clinical implementation of artificial intelligence based synthetic computed tomography for magnetic resonance only radiotherapy.

Synthetic computed tomography (sCT) generated from magnetic resonance imaging (MRI) can serve as a substitute for planning CT in radiation therapy (RT), thereby removing registration uncertainties associated with multi-modality imaging pairing, reducing costs and patient radiation exposure. CE/FDA-approved sCT solutions are nowadays available for pelvis, brain, and head and neck, while more complex deep learning (DL) algorithms are under investigation for other anatomic sites. The main challenge in achieving a widespread clinical implementation of sCT lies in the absence of consensus on sCT commissioning and quality assurance (QA), resulting in variation of sCT approaches across different hospitals. To address this issue, a group of experts gathered at the ESTRO Physics Workshop 2022 to discuss the integration of sCT solutions into clinics and report the process and its outcomes. This position paper focuses on aspects of sCT development and commissioning, outlining key elements crucial for the safe implementation of an MRI-only RT workflow.

Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example.

BACKGROUND AND PURPOSE: Studies investigating the application of Artificial Intelligence (AI) in the field of radiotherapy exhibit substantial variations in terms of quality. The goal of this study was to assess the amount of transparency and bias in scoring articles with a specific focus on AI based segmentation and treatment planning, using modified PROBAST and TRIPOD checklists, in order to provide recommendations for future guideline developers and reviewers. MATERIALS AND METHODS: The TRIPOD and PROBAST checklist items were discussed and modified using a Delphi process. After consensus was reached, 2 groups of 3 co-authors scored 2 articles to evaluate usability and further optimize the adapted checklists. Finally, 10 articles were scored by all co-authors. Fleiss' kappa was calculated to assess the reliability of agreement between observers. RESULTS: Three of the 37 TRIPOD items and 5 of the 32 PROBAST items were deemed irrelevant. General terminology in the items (e.g., multivariable prediction model, predictors) was modified to align with AI-specific terms. After the first scoring round, further improvements of the items were formulated, e.g., by preventing the use of sub-questions or subjective words and adding clarifications on how to score an item. Using the final consensus list to score the 10 articles, only 2 out of the 61 items resulted in a statistically significant kappa of 0.4 or more demonstrating substantial agreement. For 41 items no statistically significant kappa was obtained indicating that the level of agreement among multiple observers is due to chance alone. CONCLUSION: Our study showed low reliability scores with the adapted TRIPOD and PROBAST checklists. Although such checklists have shown great value during development and reporting, this raises concerns about the applicability of such checklists to objectively score scientific articles for AI applications. When developing or revising guidelines, it is essential to consider their applicability to score articles without introducing bias.

Real-time motion management in MRI-guided radiotherapy: Current status and AI-enabled prospects.

MRI-guided radiotherapy (MRIgRT) is a highly complex treatment modality, allowing adaptation to anatomical changes occurring from one treatment day to the other (inter-fractional), but also to motion occurring during a treatment fraction (intra-fractional). In this vision paper, we describe the different steps of intra-fractional motion management during MRIgRT, from imaging to beam adaptation, and the solutions currently available both clinically and at a research level. Furthermore, considering the latest developments in the literature, a workflow is foreseen in which motion-induced over- and/or under-dosage is compensated in 3D, with minimal impact to the radiotherapy treatment time. Considering the time constraints of real-time adaptation, a particular focus is put on artificial intelligence (AI) solutions as a fast and accurate alternative to conventional algorithms.

Results of a multicenter 4D computed tomography quality assurance audit: Evaluating image accuracy and consistency.

Background and purpose: 4D Computed Tomography (4DCT) technology captures the location and movement of tumors and nearby organs at risk over time. In this study, a multi-institutional multi-vendor 4DCT audit was initiated to assess the accuracy of current imaging protocols. Materials and methods: Twelve centers, including thirteen scanners performed a 4DCT acquisition of a dynamic thorax phantom using the institution's own protocol with the in-house breathing monitoring system. Five regular and three irregular breathing patterns were used. Image acquisition and reconstruction were followed by automated image analysis with our in-house developed 4DCT QA program (QAMotion). CT number accuracy, volume deviation, amplitude deviation, and spatial integrity were assessed per pattern using both the segmented volumes and line profiles. Results: Regular breathing curves showed relatively accurate results across all institutions, with mean volume and CT number deviations and median amplitude deviation below 2%, 5 HU and 2 mm, respectively. Results obtained for irregular patterns showed more variation across the institutions. Volume and CT number deviations co-occurred with a blurring of the sphere, interpolation, or double-structure artifacts that were confirmed through the line profiles. For some of the irregular patterns, amplitude deviations up to 6 mm were observed. Maximum Intensity Projection (MaxIP) correctly captured the applied motion amplitude with deviations across all institutions within 2 mm except for double amplitude pattern. Conclusions: All centers invited to participate in the audit responded positively, highlighting the need for a comprehensive yet easy-to-execute 4DCT quality assurance program. The largest variances between the results from one institution to another confirmed that a standardized 4DCT audit is warranted.

A comprehensive quality assurance program for four-dimensional computed tomography in radiotherapy.

This study aimed to develop and validate a comprehensive, reproducible and automatic 4DCT Quality Assurance (QA) workflow (QAMotion) that evaluates image accuracy across various regular and irregular breathing patterns. Volume and amplitude deviations, CT number accuracy, and spatial integrity were used as evaluation metrics. For repeatability tests, tolerances were respected with a mean CT number deviation < 10 HU, volume deviation < 2% and diameter and amplitude deviation < 2 mm except for irregular amplitude curves for which an amplitude deviation up to 6 mm was measured. QAMotion was able to flag image artefacts for our clinical 4DCT system.

An impact model to understand and improve work-life balance in early-career researchers in radiation oncology.

Purpose: The COVID-19 pandemic had a substantial effect on mental health and work productivity of early-career researchers working in Radiation Oncology (RO). However, the underlying mechanisms of these effects are unclear. The aim of the current qualitative study was therefore to achieve a better understanding of how these effects arose and could be managed in the future. Methods: This study was conducted jointly by RO and qualitative health researchers. Data was collected in four online Focus Groups with 6-11 RO researchers (total N = 31) working in Europe. The transcripts were analysed through a qualitative cross-impact analysis. Results: Causal relations were identified between seventeen variables that depict the impact of disrupted working conditions. Mental health and work productivity were indeed the most important affected variables, but relations between variables towards these impacts were complex. Relations could either be positive or negative and direct or indirect, leading to a cascade of interrelated events which are highly personal and could change over time. We developed the model 'impact of disrupted working conditions' depicting the identified variables and their relations, to allow more individual assessment and personalised solutions. Conclusion: The impacts of disrupted working conditions on RO researchers varied due to the complexity of interrelated variables. Consequently, collective actions are not sufficient, and a more personal approach is needed. Our impact model is recommended to help guide conversations and reflections with the aim of improving work/life balance. The participants showed high levels of personal responsibility towards their own mental health and work productivity. Although being an individual issue, a collective responsibility in developing such approaches is key due to the dependency on organizational variables.

Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation.

PURPOSE: The aim of this study was to perform a quantitative quality assurance of diffusion-weighted MRI to assess the variability of the mean apparent diffusion coefficient (ADC) and other radiomic features across the scanners involved in the REGINA trial. MATERIALS AND METHODS: The NIST/QIBA diffusion phantom was acquired on six 3 T scanners from five centres with a rectum-specific diffusion protocol. All sequences were repeated in each scan session without moving the phantom from the table. Linear interpolation to two isotropic voxel spacing (0.9 and 4 mm) was performed as well as the ComBat feature harmonisation method between scanners. The absolute accuracy error was evaluated for the mean ADC. Repeatability and reproducibility within-subject coefficients of variation (wCV) were computed for 142 radiomic features. RESULTS: For the mean ADC, accuracy error ranged between 0.1 % and 8.5 %, repeatability was <1 % and reproducibility was <3 % for diffusivity range between 0.4 and 1.1x10-3mm2/s. For the other radiomic features, wCV was below 10 % for 24 % and 15 % features for repeatability with resampling 0.9 mm and 4 mm, respectively, and 13 % and 11 % feature for reproducibility. ComBat method could improve significantly the wCV compared to reproducibility without ComBat (p-value < 0.001) but variation was still high for most of the features. CONCLUSION: Our study provided the first investigation of feature selection for development of robust predictive models in the REGINA trial, demonstrating the added value of such a quality assurance process to select conventional and radiomic features in prospective multicentre trials.

Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values.

INTRODUCTION: Radiomics is a promising imaging-based tool which could enhance clinical observation and identify representative features. To avoid different interpretations, the Image Biomarker Standardisation Initiative (IBSI) imposed conditions for harmonisation. This study evaluates IBSI-compliant radiomics applications against a known benchmark and clinical datasets for agreements. MATERIALS AND METHODS: The three radiomics platforms compared were RadiomiX Research Toolbox, LIFEx v7.0.0, and syngo.via Frontier Radiomics v1.2.5 (based on PyRadiomics v2.1). Basic assessment included comparing feature names and their formulas. The IBSI digital phantom was used for evaluation against reference values. For agreement evaluation (including same software but different versions), two clinical datasets were used: 27 contrast-enhanced computed tomography (CECT) of colorectal liver metastases and 39 magnetic resonance imaging (MRI) of breast cancer, including intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced (DCE) MRI. The intraclass correlation coefficient (ICC, lower 95% confidence interval) was used, with 0.9 as the threshold for excellent agreement. RESULTS: The three radiomics applications share 41 (3 shape, 8 intensity, 30 texture) out of 172, 84 and 110 features for RadiomiX, LIFEx and syngo.via, respectively, as well as wavelet filtering. The naming convention is, however, different between them. Syngo.via had excellent agreement with the IBSI benchmark, while LIFEx and RadiomiX showed slightly worse agreement. Excellent reproducibility was achieved for shape features only, while intensity and texture features varied considerably with the imaging type. For intensity, excellent agreement ranged from 46% for the DCE maps to 100% for CECT, while this lowered to 44% and 73% for texture features, respectively. Wavelet features produced the greatest variation between applications, with an excellent agreement for only 3% to 11% features. CONCLUSION: Even with IBSI-compliance, the reproducibility of features between radiomics applications is not guaranteed. To evaluate variation, quality assurance of radiomics applications should be performed and repeated when updating to a new version or adding a new modality.

The Road to Dissemination: The Concept of Oligometastases and the Barriers for Widespread Disease.

Over the last years, the oligometastatic disease state has gained more and more interest, and randomized trials are now suggesting an added value of stereotactic radiotherapy on all macroscopic disease in oligometastatic patients; but what barriers could impede widespread disease in some patients? In this review, we first discuss the concept of oligometastatic disease and some examples of clinical evidence. We then explore the route to dissemination: the hurdles a tumoral clone has to overtake before it can produce efficient and widespread dissemination. The spectrum theory argues that the range of metastatic patterns encountered in the clinic is the consequence of gradually obtained metastatic abilities of the tumor cells. Tumor clones can obtain these capabilities by Darwinian evolution, hence early in their genetic progression tumors might produce only a limited number of metastases. We illustrate selective dissemination by discussing organ tropism, the preference of different cancer (sub)types to metastasize to certain organs. Finally we discuss biomarkers that may help to distinguish the oligometastatic state.

Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?

PURPOSE: Over the last 2 years, the artificial intelligence (AI) community has presented several automatic screening tools for coronavirus disease 2019 (COVID-19) based on chest radiography (CXR), with reported accuracies often well over 90%. However, it has been noted that many of these studies have likely suffered from dataset bias, leading to overly optimistic results. The purpose of this study was to thoroughly investigate to what extent biases have influenced the performance of a range of previously proposed and promising convolutional neural networks (CNNs), and to determine what performance can be expected with current CNNs on a realistic and unbiased dataset. METHODS: Five CNNs for COVID-19 positive/negative classification were implemented for evaluation, namely VGG19, ResNet50, InceptionV3, DenseNet201, and COVID-Net. To perform both internal and cross-dataset evaluations, four datasets were created. The first dataset Valencian Region Medical Image Bank (BIMCV) followed strict reverse transcriptase-polymerase chain reaction (RT-PCR) test criteria and was created from a single reliable open access databank, while the second dataset (COVIDxB8) was created through a combination of six online CXR repositories. The third and fourth datasets were created by combining the opposing classes from the BIMCV and COVIDxB8 datasets. To decrease inter-dataset variability, a pre-processing workflow of resizing, normalization, and histogram equalization were applied to all datasets. Classification performance was evaluated on unseen test sets using precision and recall. A qualitative sanity check was performed by evaluating saliency maps displaying the top 5%, 10%, and 20% most salient segments in the input CXRs, to evaluate whether the CNNs were using relevant information for decision making. In an additional experiment and to further investigate the origin of potential dataset bias, all pixel values outside the lungs were set to zero through automatic lung segmentation before training and testing. RESULTS: When trained and evaluated on the single online source dataset (BIMCV), the performance of all CNNs is relatively low (precision: 0.65-0.72, recall: 0.59-0.71), but remains relatively consistent during external evaluation (precision: 0.58-0.82, recall: 0.57-0.72). On the contrary, when trained and internally evaluated on the combinatory datasets, all CNNs performed well across all metrics (precision: 0.94-1.00, recall: 0.77-1.00). However, when subsequently evaluated cross-dataset, results dropped substantially (precision: 0.10-0.61, recall: 0.04-0.80). For all datasets, saliency maps revealed the CNNs rarely focus on areas inside the lungs for their decision-making. However, even when setting all pixel values outside the lungs to zero, classification performance does not change and dataset bias remains. CONCLUSIONS: Results in this study confirm that when trained on a combinatory dataset, CNNs tend to learn the origin of the CXRs rather than the presence or absence of disease, a behavior known as short-cut learning. The bias is shown to originate from differences in overall pixel values rather than embedded text or symbols, despite consistent image pre-processing. When trained on a reliable, and realistic single-source dataset in which non-lung pixels have been masked, CNNs currently show limited sensitivity (<70%) for COVID-19 infection in CXR, questioning their use as a reliable automatic screening tool.

Artificial Intelligence in magnetic Resonance guided Radiotherapy: Medical and physical considerations on state of art and future perspectives.

Over the last years, technological innovation in Radiotherapy (RT) led to the introduction of Magnetic Resonance-guided RT (MRgRT) systems. Due to the higher soft tissue contrast compared to on-board CT-based systems, MRgRT is expected to significantly improve the treatment in many situations. MRgRT systems may extend the management of inter- and intra-fraction anatomical changes, offering the possibility of online adaptation of the dose distribution according to daily patient anatomy and to directly monitor tumor motion during treatment delivery by means of a continuous cine MR acquisition. Online adaptive treatments require a multidisciplinary and well-trained team, able to perform a series of operations in a safe, precise and fast manner while the patient is waiting on the treatment couch. Artificial Intelligence (AI) is expected to rapidly contribute to MRgRT, primarily by safely and efficiently automatising the various manual operations characterizing online adaptive treatments. Furthermore, AI is finding relevant applications in MRgRT in the fields of image segmentation, synthetic CT reconstruction, automatic (on-line) planning and the development of predictive models based on daily MRI. This review provides a comprehensive overview of the current AI integration in MRgRT from a medical physicist's perspective. Medical physicists are expected to be major actors in solving new tasks and in taking new responsibilities: their traditional role of guardians of the new technology implementation will change with increasing emphasis on the managing of AI tools, processes and advanced systems for imaging and data analysis, gradually replacing many repetitive manual tasks.

RealDRR - Rendering of realistic digitally reconstructed radiographs using locally trained image-to-image translation.

INTRODUCTION: Digitally reconstructed radiographs (DRRs) represent valuable patient-specific pre-treatment training data for tumor tracking algorithms. However, using current rendering methods, the similarity of the DRRs to real X-ray images is limited, requires time-consuming measurements and/or are computationally expensive. In this study we present RealDRR, a novel framework for highly realistic and computationally efficient DRR rendering. MATERIALS AND METHODS: RealDRR consists of two components applied sequentially to render a DRR. First, a raytracer is applied for forward projection from 3D CT data to a 2D image. Second, a conditional Generative Adverserial Network (cGAN) is applied to translate the 2D forward projection to a realistic 2D DRR. The planning CT and CBCT projections from a CIRS thorax phantom and 6 radiotherapy patients (3 prostate, 3 brain) were split in training and test sets for evaluating the intra-patient, inter-patient and inter-anatomical region generalization performance of the trained framework. Several image similarity metrics, as well as a verification based on template matching, were used between the rendered DRRs and respective CBCT projections in the test sets, and results were compared to those of a current state-of-the-art DRR rendering method. RESULTS: When trained on 800 CBCT projection images from two patients and tested on a third unseen patient from either anatomical region, RealDRR outperformed the current state-of-the-art with statistical significance on all metrics (two-sample t-test, p < 0.05). Once trained, the framework is able to render 100 highly realistic DRRs in under two minutes. CONCLUSION: A novel framework for realistic and efficient DRR rendering was proposed. As the framework requires a reasonable amount of computational resources, the internal parameters can be tailored to imaging systems and protocols through on-site training on retrospective imaging data.

Conducting research in Radiation Oncology remotely during the COVID-19 pandemic: Coping with isolation.

INTRODUCTION: With the COVID-19 pandemic, individuals have been forced to follow strict social isolation guidelines. While crucial to control the pandemic, isolation might have a significant impact on productivity and mental health. Especially for researchers working in healthcare, the current situation is complex. We therefore carried out a survey amongst researchers in the field of radiation oncology to gain insights on the impact of social isolation and working from home and to guide future work. MATERIALS AND METHODS: An online survey was conducted between March 27th and April 5th, 2020. The first part contained 14 questions intended to capture an overview of the specific aspects related to research while in isolation. The second (optional) part of the questionnaire was the validated Hospital Anxiety and Depression Scale (HADS), a self-reported measure used to assess levels of anxiety and depressive symptoms. RESULTS: From 543 survey participants, 48.8% reported to work full-time from home. The impact on perceived productivity, with 71.2% of participants feeling less productive, caused 58% of participants to feel some level of guilt.Compared to normative data, relatively high levels of anxiety and depressive symptoms were recorded for the 335 participants who filled out the HADS questionnaire. Group comparisons found the presence of a supportive institutional program as the sole factor of statistical significance in both anxiety and depressive symptom levels. People having to work full-time on location showed higher depressive symptom levels than those working from home. Anxiety scores were negatively correlated with the number of research years. CONCLUSION: Results of the survey showed there is a non-negligible impact on both productivity and mental health. As the radiation oncology research community was forced to work from home during the COVID-19 pandemic, lessons can be learned to face future adverse situations but also to improve work-life balance in general.

Reliability of ITV approach to varying treatment fraction time: a retrospective analysis based on 2D cine MR images.

BACKGROUND: Internal Target Volume (ITV) is one of the most common strategies to passively manage tumour motion in Radiotherapy (RT). The reliability of this approach is based on the assumption that the tumour motion estimated during pre-treatment 4D Computed Tomography (CT) acquisition is representative of the motion during the whole RT treatment. With the introduction of Magnetic Resonance-guided RT (MRgRT), it has become possible to monitor tumour motion during the treatment and verify this assumption. Aim of this study was to investigate the reliability of the ITV approach with respect to the treatment fraction time (TFT) in abdominal and thoracic lesions. METHODS: A total of 12 thoracic and 15 abdominal lesions was analysed. Before treatment, a 10-phase 4DCT was acquired and ITV margins were estimated considering the envelope of the lesion contoured on the different 4DCT phases. All patients underwent MRgRT treatment in free-breathing, monitoring the tumour position on a sagittal plane with 4 frames per second (sec). ITV margins were projected on the tumour trajectory and the percentage of treatment time in which the tumour was inside the ITV (%TT) was measured to varying of TFT. The ITV approach was considered moderately reliable when %TT ≥ 90% and strongly reliable when %TT ≥ 95%. Additional ITV margins required to achieve %TT ≥ 95% were also calculated. RESULTS: In the analysed cohort of patients, ITV strategy can be considered strongly reliable only for lung lesions with TFT ≤ 7 min (min). The ITV strategy can be considered only moderately reliable for abdominal lesions, and additional margins are required to obtain %TT ≥ 95%. Considering a TFT ≤ 4 min, additional margins of 2 mm in cranio-caudal (CC) and 1 mm in antero-posterior (AP) are suggested for pancreatic lesions, 3 mm in CC and 2 mm in AP for renal and liver ones. CONCLUSIONS: On the basis of the analysed cases, the ITV approach appears to be reliable in the thorax, while it results more challenging in the abdomen, due to the higher uncertainty in ITV definition and to the observed larger intra and inter-fraction motion variability. The addition of extra margins based on the TFT may represent a valid tool to compensate such limitations.

Multi-object tracking in MRI-guided radiotherapy using the tracking-learning-detection framework.

The application of the tracking-learning-detection (TLD) framework; a performant tracking algorithm for real-life objects in CCD video, was evaluated and successfully optimized for tracking anatomical structures in low-quality 2D cine-MRI acquired during MRI-guided radiotherapy. Sub-pixel tracking accuracy and >95% precision and recall was achieved despite significant deformations and periodical disappearance.

Predicting tumour motion during the whole radiotherapy treatment: a systematic approach for thoracic and abdominal lesions based on real time MR.

INTRODUCTION: Aim of this study was to investigate the ability of pre-treatment four dimensional computed tomography (4DCT) to capture respiratory-motion observed in thoracic and abdominal lesions during treatment. Treatment motion was acquired using full-treatment cine-MR acquisitions. Results of this analysis were compared to the ability of 30 seconds (s) cine Magnetic Resonance (MR) to estimate the same parameters. METHODS: A 4DCT and 30 s cine-MR (ViewRay, USA) were acquired on the simulation day for 7 thoracic and 13 abdominal lesions. Mean amplitude, intra- and inter-fraction amplitude variability, and baseline drift were extracted from the full treatment data acquired by 2D cine-MR, and correlated to the motion on pre-treatment 30 s cine-MR and 4DCT. Using the full treatment data, safety margins on the ITV, necessary to account for all motion variability from 4DCT observed during treatment, were calculated. Mean treatment amplitudes were 2 ±â€¯1 mm and 5 ±â€¯3 mm in the anteroposterior (AP) and craniocaudal (CC) direction, respectively. Differences between mean amplitude during treatment and amplitude on 4DCT or during 30 s cine-MR were not significant, but 30 s cine-MR was more accurate than 4DCT. Intra-fraction amplitude variability was positively correlated with both 30 s cine-MR and 4DCT amplitude. Inter-fraction amplitude variability was minimal. RESULTS: Mean baseline drift over all fractions and patients equalled 1 ±â€¯1 mm in both CC and AP direction, but drifts per fraction up to 16 mm (CC) and 12 mm (AP) were observed. Margins necessary on the ITV ranged from 0 to 8 mm in CC and 0 to 5 mm in AP direction. Neither amplitude on 4DCT nor during 30 s cine MR is correlated to the magnitude of drift or the necessary margins in both directions. CONCLUSION: Lesions moving with small amplitude show limited amplitude variability throughout treatment, making passive motion management strategies seem adequate. However, other variations such as baseline drifts and shifts still cause significant geometrical uncertainty, favouring real-time monitoring and an active approach for all lesions influenced by respiratory motion.

The long- and short-term variability of breathing induced tumor motion in lung and liver over the course of a radiotherapy treatment.

PURPOSE: To evaluate the short and long-term variability of breathing induced tumor motion. MATERIALS AND METHODS: 3D tumor motion of 19 lung and 18 liver lesions captured over the course of an SBRT treatment were evaluated and compared to the motion on 4D-CT. An implanted fiducial could be used for unambiguous motion information. Fast orthogonal fluoroscopy (FF) sequences, included in the treatment workflow, were used to evaluate motion during treatment. Several motion parameters were compared between different FF sequences from the same fraction to evaluate the intrafraction variability. To assess interfraction variability, amplitude and hysteresis were compared between fractions and with the 3D tumor motion registered by 4D-CT. Population based margins, necessary on top of the ITV to capture all motion variability, were calculated based on the motion captured during treatment. RESULTS: Baseline drift in the cranio-caudal (CC) or anterior-poster (AP) direction is significant (ie. >5 mm) for a large group of patients, in contrary to intrafraction amplitude and hysteresis variability. However, a correlation between intrafraction amplitude variability and mean motion amplitude was found (Pearson's correlation coefficient, r = 0.72, p < 10-4). Interfraction variability in amplitude is significant for 46% of all lesions. As such, 4D-CT accurately captures the motion during treatment for some fractions but not for all. Accounting for motion variability during treatment increases the PTV margins in all directions, most significantly in CC from 5 mm to 13.7 mm for lung and 8.0 mm for liver. CONCLUSION: Both short-term and day-to-day tumor motion variability can be significant, especially for lesions moving with amplitudes above 7 mm. Abandoning passive motion management strategies in favor of more active ones is advised.

Treating patients with Dynamic Wave Arc: First clinical experience.

BACKGROUND AND PURPOSE: Dynamic Wave Arc (DWA) is a system-specific noncoplanar arc technique that combines synchronized gantry-ring rotation with D-MLC optimization. This paper presents the clinical workflow, quality assurance program, and reports the geometric and dosimetric results of the first patient cohort treated with DWA. METHODS AND MATERIALS: The RayStation TPS was clinically integrated on the Vero SBRT platform for DWA treatments. The first 15 patients treated with DWA represent a broad range of treatment sites: breast boost, prostate, lung SBRT and bone metastases, which allowed us to explore the potentials and assess the limitations of the current DWA site-specific template solution. For the DWA verification a variety of QA equipment was used, from 3D diode array to an anthropomorphic end-to-end phantom. The geometric accuracy of each arc was verified with an independent orthogonal fluoroscopy method. RESULTS: The average beam-on delivery time was 3min, ranging from 1.22min to 8.82min. All patient QAs passed our institutional clinical criteria of gamma index. For both EBT3 film and Delta4 measurements, DWA planned versus delivered dose distributions presented an average agreement above 97%. An overall mean gantry-ring geometric deviation of -0.03°±0.46° and 0.18°±0.26° was obtained, respectively. CONCLUSION: For the first time, DWA has been translated into the clinic and used to treat various treatment sides. DWA has been successfully added to the noncoplanar rotational IMRT techniques arsenal, allowing additional flexibility in dose shaping while preserving dosimetrically robust delivery.

Initial characterization, dosimetric benchmark and performance validation of Dynamic Wave Arc.

BACKGROUND: Dynamic Wave Arc (DWA) is a clinical approach designed to maximize the versatility of Vero SBRT system by synchronizing the gantry-ring noncoplanar movement with D-MLC optimization. The purpose of this study was to verify the delivery accuracy of DWA approach and to evaluate the potential dosimetric benefits. METHODS: DWA is an extended form of VMAT with a continuous varying ring position. The main difference in the optimization modules of VMAT and DWA is during the angular spacing, where the DWA algorithm does not consider the gantry spacing, but only the Euclidian norm of the ring and gantry angle. A preclinical version of RayStation v4.6 (RaySearch Laboratories, Sweden) was used to create patient specific wave arc trajectories for 31 patients with various anatomical tumor regions (prostate, oligometatstatic cases, centrally-located non-small cell lung cancer (NSCLC) and locally advanced pancreatic cancer-LAPC). DWA was benchmarked against the current clinical approaches and coplanar VMAT. Each plan was evaluated with regards to dose distribution, modulation complexity (MCS), monitor units and treatment time efficiency. The delivery accuracy was evaluated using a 2D diode array that takes in consideration the multi-dimensionality of DWA during dose reconstruction. RESULTS: In centrally-located NSCLC cases, DWA improved the low dose spillage with 20 %, while the target coverage was increased with 17 % compared to 3D CRT. The structures that significantly benefited from using DWA were proximal bronchus and esophagus, with the maximal dose being reduced by 17 % and 24 %, respectively. For prostate and LAPC, neither technique seemed clearly superior to the other; however, DWA reduced with more than 65 % of the delivery time over IMRT. A steeper dose gradient outside the target was observed for all treatment sites (p < 0.01) with DWA. Except the oligometastatic cases, where the DWA-MCSs indicate a higher modulation, both DWA and VMAT modalities provide plans of similar complexity. The average É£ (3 % /3 mm) passing rate for DWA plans was 99.2 ± 1 % (range from 96.8 to 100 %). CONCLUSIONS: DWA proven to be a fully functional treatment technique, allowing additional flexibility in dose shaping, while preserving dosimetrically robust delivery and treatment times comparable with coplanar VMAT.

Motion management during SBRT for oligometastatic cancer: Results of a prospective phase II trial.

PURPOSE: To optimize the local control of stereotactic body radiotherapy (SBRT) using the Vero-SBRT system and respiratory motion management in patients with oligometastatic cancer. MATERIALS AND METHODS: Patients with five or less metastases were eligible. In metastases with significant motion, a fiducial was implanted for Vero dynamic tracking. For other metastases an internal target volume (ITV) was defined to encompass the respiratory tumor trajectory. A dose of 50Gy in 10 fractions was prescribed on the 80% isodose line. RESULTS: We treated 87 metastases in 44 patients, with colorectal cancer as the most common primary origin (65.9%). Metastatic sites were mainly lung (n=62) and liver (n=17). Twenty-seven metastases were treated with dynamic tracking, the remaining 60 using the ITV-concept. Three patients (7%) experienced grade ⩾3 toxicity. After a median follow-up of 12months, the overall one-year local control (LC) amounted to 89% (95% CI 77-95%), with corresponding values of 90% and 88% for the metastases irradiated with the ITV-approach and dynamic tracking, respectively. Median progression-free survival reached 6.5months, one-year overall survival 95%. CONCLUSIONS: SBRT with proper respiratory motion management resulted in a high LC and an acceptable toxicity profile in oligometastatic cancer patients.