CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT.

Objective.In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e.bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.Approach.DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson's correlation and Czekanowski's index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon's signed rank test and the Kolmogorov-Smirnov two sample test returnedp≤ 0.05 for both tests.Main results.Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with allp≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with allp< 10-6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.Significance.Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system's DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

The first clinical implementation of real-time 6 degree-of-freedom image-guided radiotherapy for liver SABR patients.

PURPOSE: Multiple survey results have identified a demand for improved motion management for liver cancer IGRT. Until now, real-time IGRT for liver has been the domain of dedicated and expensive cancer radiotherapy systems. The purpose of this study was to clinically implement and characterise the performance of a novel real-time 6 degree-of-freedom (DoF) IGRT system, Kilovoltage Intrafraction Monitoring (KIM) for liver SABR patients. METHODS/MATERIALS: The KIM technology segmented gold fiducial markers in intra-fraction x-ray images as a surrogate for the liver tumour and converted the 2D segmented marker positions into a real-time 6DoF tumour position. Fifteen liver SABR patients were recruited and treated with KIM combined with external surrogate guidance at three radiotherapy centres in the TROG 17.03 LARK multi-institutional prospective clinical trial. Patients were either treated in breath-hold or in free breathing using the gating method. The KIM localisation accuracy and dosimetric accuracy achieved with KIM + external surrogate were measured and the results were compared to those with the estimated external surrogate alone. RESULTS: The KIM localisation accuracy was 0.2±0.9 mm (left-right), 0.3±0.6 mm (superior-inferior) and 1.2±0.8 mm (anterior-posterior) for translations and -0.1◦±0.8◦ (left-right), 0.6◦±1.2◦ (superior-inferior) and 0.1◦±0.9◦ (anterior-posterior) for rotations. The cumulative dose to the GTV with KIM + external surrogate was always within 5% of the plan. In 2 out of 15 patients, >5% dose error would have occurred to the GTV and an organ-at-risk with external surrogate alone. CONCLUSIONS: This work demonstrates that real-time 6DoF IGRT for liver can be implemented on standard radiotherapy systems to improve treatment accuracy and safety. The observations made during the treatments highlight the potential false assurance of using traditional external surrogates to assess tumour motion in patients and the need for ongoing improvement of IGRT technologies.

Realistic CT data augmentation for accurate deep-learning based segmentation of head and neck tumors in kV images acquired during radiation therapy.

BACKGROUND: Using radiation therapy (RT) to treat head and neck (H&N) cancers requires precise targeting of the tumor to avoid damaging the surrounding healthy organs. Immobilisation masks and planning target volume margins are used to attempt to mitigate patient motion during treatment, however patient motion can still occur. Patient motion during RT can lead to decreased treatment effectiveness and a higher chance of treatment related side effects. Tracking tumor motion would enable motion compensation during RT, leading to more accurate dose delivery. PURPOSE: The purpose of this paper is to develop a method to detect and segment the tumor in kV images acquired during RT. Unlike previous tumor segmentation methods for kV images, in this paper, a process for generating realistic and synthetic CT deformations was developed to augment the training data and make the segmentation method robust to patient motion. Detecting the tumor in 2D kV images is a necessary step toward 3D tracking of the tumor position during treatment. METHOD: In this paper, a conditional generative adversarial network (cGAN) is presented that can detect and segment the gross tumor volume (GTV) in kV images acquired during H&N RT. Retrospective data from 15 H&N cancer patients obtained from the Cancer Imaging Archive were used to train and test patient-specific cGANs. The training data consisted of digitally reconstructed radiographs (DRRs) generated from each patient's planning CT and contoured GTV. Training data was augmented by using synthetically deformed CTs to generate additional DRRs (in total 39 600 DRRs per patient or 25 200 DRRs for nasopharyngeal patients) containing realistic patient motion. The method for deforming the CTs was a novel deformation method based on simulating head rotation and internal tumor motion. The testing dataset consisted of 1080 DRRs for each patient, obtained by deforming the planning CT and GTV at different magnitudes to the training data. The accuracy of the generated segmentations was evaluated by measuring the segmentation centroid error, Dice similarity coefficient (DSC) and mean surface distance (MSD). This paper evaluated the hypothesis that when patient motion occurs, using a cGAN to segment the GTV would create a more accurate segmentation than no-tracking segmentations from the original contoured GTV, the current standard-of-care. This hypothesis was tested using the 1-tailed Mann-Whitney U-test. RESULTS: The magnitude of our cGAN segmentation centroid error was (mean ± standard deviation) 1.1 ± 0.8 mm and the DSC and MSD values were 0.90 ± 0.03 and 1.6 ± 0.5 mm, respectively. Our cGAN segmentation method reduced the segmentation centroid error (p < 0.001), and MSD (p = 0.031) when compared to the no-tracking segmentation, but did not significantly increase the DSC (p = 0.294). CONCLUSIONS: The accuracy of our cGAN segmentation method demonstrates the feasibility of this method for H&N cancer patients during RT. Accurate tumor segmentation of H&N tumors would allow for intrafraction monitoring methods to compensate for tumor motion during treatment, ensuring more accurate dose delivery and enabling better H&N cancer patient outcomes.

Deep learning enables MV-based real-time image guided radiation therapy for prostate cancer patients.

Objective. Using MV images for real-time image guided radiation therapy (IGRT) is ideal as it does not require additional imaging equipment, adds no additional imaging dose and provides motion data in the treatment beam frame of reference. However, accurate tracking using MV images is challenging due to low contrast and modulated fields. Here, a novel real-time marker tracking system based on a convolutional neural network (CNN) classifier was developed and evaluated on retrospectively acquired patient data for MV-based IGRT for prostate cancer patients.Approach. MV images, acquired from 29 volumetric modulated arc therapy (VMAT) prostate cancer patients treated in a multi-institutional clinical trial, were used to train and evaluate a CNN-based marker tracking system. The CNN was trained using labelled MV images from 9 prostate cancer patients (35 fractions) with implanted markers. CNN performance was evaluated on an independent cohort of unseen MV images from 20 patients (78 fractions), using a Precision-Recall curve (PRC), area under the PRC plot (AUC) and sensitivity and specificity. The accuracy of the tracking system was evaluated on the same unseen dataset and quantified by calculating mean absolute (±1 SD) and [1st, 99th] percentiles of the geometric tracking error in treatment beam co-ordinates using manual identification as the ground truth.Main results. The CNN had an AUC of 0.99, sensitivity of 98.31% and specificity of 99.87%. The mean absolute geometric tracking error was 0.30 ± 0.27 and 0.35 ± 0.31 mm in the lateral and superior-inferior directions of the MV images, respectively. The [1st, 99th] percentiles of the error were [-1.03, 0.90] and [-1.12, 1.12] mm in the lateral and SI directions, respectively.Significance. The high classification performance on unseen MV images demonstrates the CNN can successfully identify implanted prostate markers. Furthermore, the sub-millimetre accuracy and precision of the marker tracking system demonstrates potential for adaptation to real-time applications.

The dosimetric error due to uncorrected tumor rotation during real-time adaptive prostate stereotactic body radiation therapy.

BACKGROUND: During prostate stereotactic body radiation therapy (SBRT), prostate tumor translational motion may deteriorate the planned dose distribution. Most of the major advances in motion management to date have focused on correcting this one aspect of the tumor motion, translation. However, large prostate rotation up to 30° has been measured. As the technological innovation evolves toward delivering increasingly precise radiotherapy, it is important to quantify the clinical benefit of translational and rotational motion correction over translational motion correction alone. PURPOSE: The purpose of this work was to quantify the dosimetric impact of intrafractional dynamic rotation of the prostate measured with a six degrees-of-freedom tumor motion monitoring technology. METHODS: The delivered dose was reconstructed including (a) translational and rotational motion and (b) only translational motion of the tumor for 32 prostate cancer patients recruited on a 5-fraction prostate SBRT clinical trial. Patients on the trial received 7.25 Gy in a treatment fraction. A 5 mm clinical target volume (CTV) to planning target volume (PTV) margin was applied in all directions except the posterior direction where a 3 mm expansion was used. Prostate intrafractional translational motion was managed using a gating strategy, and any translation above the gating threshold was corrected by applying an equivalent couch shift. The residual translational motion is denoted as T r e s $T_{res}$ . Prostate intrafractional rotational motion R u n c o r r $R_{uncorr}$ was recorded but not corrected. The dose differences from the planned dose due to T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ , ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and due to T r e s $T_{res}$ alone, ΔD( T r e s $T_{res}$ ), were then determined for CTV D98, PTV D95, bladder V6Gy, and rectum V6Gy. The residual dose error due to uncorrected rotation, R u n c o r r $R_{uncorr}$ was then quantified: Δ D R e s i d u a l $\Delta D_{Residual}$ = ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) - ΔD( T res ${T}_{\textit{res}}$ ). RESULTS: Fractional data analysis shows that the dose differences from the plan (both ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and ΔD( T r e s $T_{res}$ )) for CTV D98 was less than 5% in all treatment fractions. ΔD( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) was larger than 5% in one fraction for PTV D95, in one fraction for bladder V6Gy, and in five fractions for rectum V6Gy. Uncorrected rotation, R u n c o r r $R_{uncorr}$ induced residual dose error, Δ D R e s i d u a l $\Delta D_{Residual}$ , resulted in less dose to CTV and PTV in 43% and 59% treatment fractions, respectively, and more dose to bladder and rectum in 51% and 53% treatment fractions, respectively. The cumulative dose over five fractions, ∑D( T r e s $T_{res}$ + R u n c o r r $R_{uncorr}$ ) and ∑D( T r e s $T_{res}$ ), was always within 5% of the planned dose for all four structures for every patient. CONCLUSIONS: The dosimetric impact of tumor rotation on a large prostate cancer patient cohort was quantified in this study. These results suggest that the standard 3-5 mm CTV-PTV margin was sufficient to account for the intrafraction prostate rotation observed for this cohort of patients, provided an appropriate gating threshold was applied to correct for translational motion. Residual dose errors due to uncorrected prostate rotation were small in magnitude, which may be corrected using different treatment adaptation strategies to further improve the dosimetric accuracy.

Optimising multi-target multileaf collimator tracking using real-time dose for locally advanced prostate cancer patients.

Objective. The accuracy of radiotherapy for patients with locally advanced cancer is compromised by independent motion of multiple targets. To date, MLC tracking approaches have used 2D geometric optimisation where the MLC aperture shape is simply translated to correspond to the target's motion, which results in sub-optimal delivered dose. To address this limitation, a dose-optimised multi-target MLC tracking method was developed and evaluated through simulated locally advanced prostate cancer treatments.Approach. A dose-optimised multi-target tracking algorithm that adapts the MLC aperture to minimise 3D dosimetric error was developed for moving prostate and static lymph node targets. A fast dose calculation algorithm accumulated the planned dose to the prostate and lymph node volumes during treatment in real time, and the MLC apertures were recalculated to minimise the difference between the delivered and planned dose with the included motion. Dose-optimised tracking was evaluated by simulating five locally advanced prostate plans and three prostate motion traces with a relative interfraction displacement. The same simulations were performed using geometric-optimised tracking and no tracking. The dose-optimised, geometric-optimised, and no tracking results were compared with the planned doses using a 2%/2 mmγcriterion.Main results. The mean dosimetric error was lowest for dose-optimised MLC tracking, withγ-failure rates of 12% ± 8.5% for the prostate and 2.2% ± 3.2% for the nodes. Theγ-failure rates for geometric-optimised MLC tracking were 23% ± 12% for the prostate and 3.6% ± 2.5% for the nodes. When no tracking was used, theγ-failure rates were 37% ± 28% for the prostate and 24% ± 3.2% for the nodes.Significance. This study developed a dose-optimised multi-target MLC tracking method that minimises the difference between the planned and delivered doses in the presence of intrafraction motion. When applied to locally advanced prostate cancer, dose-optimised tracking showed smaller errors than geometric-optimised tracking and no tracking for both the prostate and nodes.

A review of artificial intelligence applications for motion tracking in radiotherapy.

During radiotherapy, the organs and tumour move as a result of the dynamic nature of the body; this is known as intrafraction motion. Intrafraction motion can result in tumour underdose and healthy tissue overdose, thereby reducing the effectiveness of the treatment while increasing toxicity to the patients. There is a growing appreciation of intrafraction target motion management by the radiation oncology community. Real-time image-guided radiation therapy (IGRT) can track the target and account for the motion, improving the radiation dose to the tumour and reducing the dose to healthy tissue. Recently, artificial intelligence (AI)-based approaches have been applied to motion management and have shown great potential. In this review, four main categories of motion management using AI are summarised: marker-based tracking, markerless tracking, full anatomy monitoring and motion prediction. Marker-based and markerless tracking approaches focus on tracking the individual target throughout the treatment. Full anatomy algorithms monitor for intrafraction changes in the full anatomy within the field of view. Motion prediction algorithms can be used to account for the latencies due to the time for the system to localise, process and act.

A real-time IGRT method using a Kalman filter framework to extract 3D positions from 2D projections.

Purpose.To estimate 3D prostate motion in real-time during irradiation from 2D prostate positions acquired from a kV imager on a standard linear accelerator utilising a Kalman filter (KF) framework. The advantage of this novel method is threefold: (1) eliminating the need of an initial learning period, therefore reducing patient imaging dose, (2) more robust against measurement noise and (3) more computationally efficient. In this paper, the novel KF method was evaluatedin silicousing patients' 3D prostate motion and simulated 2D projections.Methods.A KF framework was implemented to estimate 3D motion from 2D projection measurements in real-time during prostate cancer treatments. The noise covariance matrix was adaptively estimated from the previous 10 measurements. This method did not require an initial learning period as the KF process distribution was initialised using a population covariance matrix. This method was evaluated using a ground-truth motion dataset of 17 prostate cancer patients (536 trajectories) measured with electromagnetic transponders. 3D motion was projected onto a rotating imager (SID = 180 cm) (pixel size = 0.388 mm) and rotation speed of 6°/s and 2°/s to simulate VMAT treatments. Gantry-varying additive random noise (≤5 mm) was added to ground-truth measurements to simulate segmentation error and image quality degradation due to the patient's pelvic bones. For comparison, motion was also estimated using the clinically implemented Gaussian probability density function (PDF) method initialised with 600 projections.Results.Without noise, the 3D root mean square-errors (3D RMSEs) of motion estimated by the KF method were 0.4 ± 0.1 mm and 0.3 ± 0.2 mm for 2°/s and 6°/s gantry rotation, respectively. With noise, 3D RMSEs of KF estimated motion were 1.1 ± 0.1 mm for both slow and fast gantry rotation scenarios. In comparison, using a Gaussian PDF method, with noise, 3D RMSE was 2 ± 0.1 mm for both gantry rotation scenarios.Conclusion.This work presents a fast and accurate method for real-time 2D to 3D motion estimation using a KF approach to handle the random-walk component of prostate cancer motion. This method has sub-mm accuracy and is highly robust against measurement noise.

Study protocol of the LARK (TROG 17.03) clinical trial: a phase II trial investigating the dosimetric impact of Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring.

BACKGROUND: Stereotactic Ablative Body Radiotherapy (SABR) is a non-invasive treatment which allows delivery of an ablative radiation dose with high accuracy and precision. SABR is an established treatment for both primary and secondary liver malignancies, and technological advances have improved its efficacy and safety. Respiratory motion management to reduce tumour motion and image guidance to achieve targeting accuracy are crucial elements of liver SABR. This phase II multi-institutional TROG 17.03 study, Liver Ablative Radiotherapy using Kilovoltage intrafraction monitoring (LARK), aims to investigate and assess the dosimetric impact of the KIM real-time image guidance technology. KIM utilises standard linear accelerator equipment and therefore has the potential to be a widely available real-time image guidance technology for liver SABR. METHODS: Forty-six patients with either hepatocellular carcinoma or oligometastatic disease to the liver suitable for and treated with SABR using Kilovoltage Intrafraction Monitoring (KIM) guidance will be included in the study. The dosimetric impact will be assessed by quantifying accumulated patient dose distribution with or without the KIM intervention. The patient treatment outcomes of local control, toxicity and quality of life will be measured. DISCUSSION: Liver SABR is a highly effective treatment, but precise dose delivery is challenging due to organ motion. Currently, there is a lack of widely available options for performing real-time tumour localisation to assist with accurate delivery of liver SABR. This study will provide an assessment of the impact of KIM as a potential solution for real-time image guidance in liver SABR. TRIAL REGISTRATION: This trial was registered on December 7th 2016 on ClinicalTrials.gov under the trial-ID NCT02984566 .

Pre-treatment and real-time image guidance for a fixed-beam radiotherapy system.

PURPOSE: A radiotherapy system with a fixed treatment beam and a rotating patient positioning system could be smaller, more robust and more cost effective compared to conventional rotating gantry systems. However, patient rotation could cause anatomical deformation and compromise treatment delivery. In this work, we demonstrate an image-guided treatment workflow with a fixed beam prototype system that accounts for deformation during rotation to maintain dosimetric accuracy. METHODS: The prototype system consists of an Elekta Synergy linac with the therapy beam orientated downward and a custom-built patient rotation system (PRS). A phantom that deforms with rotation was constructed and rotated within the PRS to quantify the performance of two image guidance techniques: motion compensated cone-beam CT (CBCT) for pre-treatment volumetric imaging and kilovoltage infraction monitoring (KIM) for real-time image guidance. The phantom was irradiated with a 3D conformal beam to evaluate the dosimetric accuracy of the workflow. RESULTS: The motion compensated CBCT was used to verify pre-treatment position and the average calculated position was within -0.3 ± 1.1 mm of the phantom's ground truth position at 0°. KIM tracked the position of the target in real-time as the phantom was rotated and the average calculated position was within -0.2 ± 0.8 mm of the phantom's ground truth position. A 3D conformal treatment delivered on the prototype system with image guidance had a 3%/2 mm gamma pass rate of 96.3% compared to 98.6% delivered using a conventional rotating gantry linac. CONCLUSIONS: In this work, we have shown that image guidance can be used with fixed-beam treatment systems to measure and account for changes in target position in order to maintain dosimetric coverage during horizontal rotation. This treatment modality could provide a viable treatment option when there insufficient space for a conventional linear accelerator or where the cost is prohibitive.

Dose-based optimisation for multi-leaf collimator tracking during radiation therapy.

Motion in the patient anatomy causes a reduction in dose delivered to the target, while increasing dose to healthy tissue. Multi-leaf collimator (MLC) tracking has been clinically implemented to adapt dose delivery to account for intrafraction motion. Current methods shift the planned MLC aperture in the direction of motion, then optimise the new aperture based on the difference in fluence. The drawback of these methods is that 3D dose, a function of patient anatomy and MLC aperture sequence, is not properly accounted for. To overcome the drawback of current fluence-based methods, we have developed and investigated real-time adaptive MLC tracking based on dose optimisation. A novel MLC tracking algorithm, dose optimisation, has been developed which accounts for the moving patient anatomy by optimising the MLC based on the dose delivered during treatment, simulated using a simplified dose calculation algorithm. The MLC tracking with dose optimisation method was applied in silico to a prostate cancer VMAT treatment dataset with observed intrafraction motion. Its performance was compared to MLC tracking with fluence optimisation and, as a baseline, without MLC tracking. To quantitatively assess performance, we computed the dose error and 3D γ failure rate (2 mm/2%) for each fraction and method. Dose optimisation achieved a γ failure rate of (4.7 ± 1.2)% (mean and standard deviation) over all fractions, which was significantly lower than fluence optimisation (7.5 ± 2.9)% (Wilcoxon sign-rank test p < 0.01). Without MLC tracking, a γ failure rate of (15.3 ± 12.9)% was achieved. By considering the accumulation of dose in the moving anatomy during treatment, dose optimisation is able to optimise the aperture to actively target regions of underdose while avoiding overdose.

MLC tracking for lung SABR is feasible, efficient and delivers high-precision target dose and lower normal tissue dose.

BACKGROUND AND PURPOSE: The purpose of this work is to present the clinical experience from the first-in-human trial of real-time tumor targeting via MLC tracking for stereotactic ablative body radiotherapy (SABR) of lung lesions. METHODS AND MATERIALS: Seventeen patients with stage 1 non-small cell lung cancer (NSCLC) or lung metastases were included in a study of electromagnetic transponder-guided MLC tracking for SABR (NCT02514512). Patients had electromagnetic transponders inserted near the tumor. An MLC tracking SABR plan was generated with planning target volume (PTV) expanded 5 mm from the end-exhale gross tumor volume (GTV). A clinically approved comparator plan was generated with PTV expanded 5 mm from a 4DCT-derived internal target volume (ITV). Treatment was delivered using a standard linear accelerator to continuously adapt the MLC based on transponder motion. Treated volumes and reconstructed delivered dose were compared between MLC tracking and comparator ITV-based treatment. RESULTS: All seventeen patients were successfully treated with MLC tracking (70 successful fractions). MLC tracking treatment delivery time averaged 8 minutes. The time from the start of CBCT to the end of treatment averaged 22 minutes. The MLC tracking PTV for 16/17 patients was smaller than the ITV-based PTV (range -1.6% to 44% reduction, or -0.6 to 18 cc). Reductions in mean lung dose (27 cGy) and V20Gy (50 cc) were statistically significant (p < 0.02). Reconstruction of treatment doses confirmed a statistically significant improvement in delivered GTV D98% (p < 0.05) from planned dose compared with the ITV-based plans. CONCLUSION: The first treatments with lung MLC tracking have been successfully performed in seventeen SABR patients. MLC tracking for lung SABR is feasible, efficient and delivers high-precision target dose and lower normal tissue dose.

Rivaroxaban Compared to Placebo for the Treatment of Leg Superficial Vein Thrombosis: A Randomized Trial.

The role of rivaroxaban in the treatment of leg superficial venous thrombosis (SVT) is uncertain. This article aims to determine if rivaroxaban is an effective and safe treatment for leg SVT. Patients with symptomatic leg SVT of at least 5 cm length were randomized to 45 days of rivaroxaban 10 mg daily or to placebo, and followed for a total of 90 days. Treatment failure (required a nonstudy anticoagulant; had proximal deep vein thrombosis or pulmonary embolism; or had surgery for SVT) at 90 days was the primary efficacy outcome. Secondary efficacy outcomes included leg pain severity, and venous disease-specific and general health-related quality of life over 90 days. Major bleeding at 90 days was the primary safety outcome. Poor enrollment led to the trial being stopped after 85 of the planned 600 patients were randomized to rivaroxaban (n = 43) or placebo (n = 42). One rivaroxaban and five placebo patients had a treatment failure by 90 days (absolute risk reduction = 9.0%, 95% confidence interval: -22 to 5.9%). Leg pain improvement did not differ at 7 (p = 0.16) or 45 days (p = 0.89), but was greater with rivaroxaban at 90 days (p = 0.011). There was no difference in venous disease-specific (p = 0.99) or general health-related (p = 0.37) quality of life over 45 days. There were no major bleeds or deaths in either group. There were no identifiable differences in efficacy or safety between rivaroxaban and placebo in patients with symptomatic SVT but comparisons were undermined by a much smaller than planned sample size (NCT1499953).

First experimental investigation of simultaneously tracking two independently moving targets on an MRI-linac using real-time MRI and MLC tracking.

PURPOSE: High quality radiotherapy is challenging in cases where multiple targets with independent motion are simultaneously treated. A real-time tumor tracking system that can simultaneously account for the motion of two targets was developed and characterized. METHODS: The multitarget tracking system was implemented on a magnetic resonance imaging (MRI)-linac and utilized multi-leaf collimator (MLC) tracking to adapt the radiation beam to phantom targets reproducing motion with prostate and lung motion traces. Multitarget tracking consisted of three stages: (a) pretreatment aperture segmentation where the treatment aperture was divided into segments corresponding to each target, (b) MR imaging where the positions of the two targets were localized, and (c) MLC tracking where an updated treatment aperture was calculated. Electronic portal images (EPID) acquired during irradiation were analyzed to characterize geometric uncertainty and tracking latency. RESULTS: Multitarget MLC tracking effectively accounted for the motion of both targets during treatment. The root-mean-square error between the centers of the targets and the centers of the corresponding MLC leaves were reduced from 5.5 mm without tracking to 2.7 mm with tracking for lung motion traces and reduced from 4.2 to 1.4 mm for prostate motion traces. The end-to-end latency of tracking was measured to be 328 ± 44 ms. CONCLUSIONS: We have demonstrated the first experimental implementation of MLC tracking for multiple targets having independent motion. This technology takes advantage of the imaging capabilities of MRI-linacs and would allow treatment margins to be reduced in cases where multiple targets are simultaneously treated.

Experimental evaluation of the dosimetric impact of intrafraction prostate rotation using film measurement with a 6DoF robotic arm.

PURPOSE: Tumor motion during radiotherapy can cause a reduction in target dose coverage and an increase in healthy tissue exposure. Tumor motion is not strictly translational and often exhibits complex six degree-of-freedom (6DoF) translational and rotational motion. Although the dosimetric impact of prostate tumor translational motion is well investigated, the dosimetric impact of 6DoF motion has only been studied with simulations or dose reconstruction. This study aims to experimentally quantify the dose error caused by 6DoF motion. The experiment was designed to test the hypothesis that 6DoF motion would cause larger dose errors than translational motion alone through gamma analyses of two-dimensional film measurements. METHODS: Four patient-measured intrafraction prostate motion traces and four VMAT 7.25 Gy/Fx SBRT treatment plans were selected for the experiment. The traces represented typical motion patterns, including small-angle rotations (<4°), transient movement, persistent excursion, and erratic rotations (>6°). Gafchromic film was placed inside a custom-designed phantom, held by a high-precision 6DoF robotic arm for dose measurements in the coronal plane during treatment delivery. For each combination of the motion trace and treatment plan, two film measurements were made, one with 6DoF motion and the other with the three-dimensional (3D) translation components of the same trace. A gamma pass rate criteria of 2% relative dose/2 mm distance-to-agreement was used in this study and evaluated for each measurement with respect to the static reference film. Two test thresholds, 90% and 50% of the reference dose, were applied to investigate the difference in dose coverage for the PTV region and surrounding areas, respectively. The hypothesis was tested using a Wilcoxon signed-rank test. RESULTS: For each of the 16 plan and motion trace pairs, a reduction in the gamma pass rate was observed for 6DoF motion compared with 3D translational motion. With 90% gamma-test threshold, the reduction was 5.8% ± 7.1% (P < 0.01). With 50% gamma-test threshold, the reduction was 4.1% ± 4.8% (P < 0.01). CONCLUSION: For the first time, the dosimetric impact of intrafraction prostate rotation during SBRT treatment was measured experimentally. The experimental results support the hypothesis that 6DoF tumor motion causes higher dose error than translation motion alone.

Real-Time Image Guided Ablative Prostate Cancer Radiation Therapy: Results From the TROG 15.01 SPARK Trial.

PURPOSE: Kilovoltage intrafraction monitoring (KIM) is a novel software platform implemented on standard radiation therapy systems and enabling real-time image guided radiation therapy (IGRT). In a multi-institutional prospective trial, we investigated whether real-time IGRT improved the accuracy of the dose patients with prostate cancer received during radiation therapy. METHODS AND MATERIALS: Forty-eight patients with prostate cancer were treated with KIM-guided SABR with 36.25 Gy in 5 fractions. During KIM-guided treatment, the prostate motion was corrected for by either beam gating with couch shifts or multileaf collimator tracking. A dose reconstruction method was used to evaluate the dose delivered to the target and organs at risk with and without real-time IGRT. Primary outcome was the effect of real-time IGRT on dose distributions. Secondary outcomes included patient-reported outcomes and toxicity. RESULTS: Motion correction occurred in ≥1 treatment for 88% of patients (42 of 48) and 51% of treatments (121 of 235). With real-time IGRT, no treatments had prostate clinical target volume (CTV) D98% dose 5% less than planned. Without real-time IGRT, 13 treatments (5.5%) had prostate CTV D98% doses 5% less than planned. The prostate CTV D98% dose with real-time IGRT was closer to the plan by an average of 1.0% (range, -2.8% to 20.3%). Patient outcomes showed no change in the 12-month patient-reported outcomes compared with baseline and no grade ≥3 genitourinary or gastrointestinal toxicities. CONCLUSIONS: Real-time IGRT is clinically effective for prostate cancer SABR.

The accuracy and precision of the KIM motion monitoring system used in the multi-institutional TROG 15.01 Stereotactic Prostate Ablative Radiotherapy with KIM (SPARK) trial.

PURPOSE: Kilovoltage intrafraction monitoring (KIM) allows for real-time image guidance for tracking tumor motion in six-degrees-of-freedom (6DoF) on a standard linear accelerator. This study assessed the geometric accuracy and precision of KIM used to guide patient treatments in the TROG 15.01 multi-institutional Stereotactic Prostate Ablative Radiotherapy with KIM trial and investigated factors affecting accuracy and precision. METHODS: Fractions from 44 patients with prostate cancer treated using KIM-guided SBRT were analyzed across four institutions, on two different linear accelerator models and two different beam models (6 MV and 10 MV FFF). The geometric accuracy and precision of KIM was assessed from over 33 000 images (translation) and over 9000 images (rotation) by comparing the real-time measured motion to retrospective kV/MV triangulation. Factors potentially affecting accuracy, including contrast-to-noise ratio (CNR) of kV images and incorrect marker segmentation, were also investigated. RESULTS: The geometric accuracy and precision did not depend on treatment institution, beam model or motion magnitude, but was correlated with gantry angle. The centroid geometric accuracy and precision of the KIM system for SABR prostate treatments was 0.0 ± 0.5, 0.0 ± 0.4 and 0.1 ± 0.3 mm for translation, and -0.1 ± 0.6°, -0.1 ± 1.4° and -0.1 ± 1.0° for rotation in the AP, LR and SI directions respectively. Centroid geometric error exceeded 2 mm for 0.05% of this dataset. No significant relationship was found between large geometric error and CNR or marker segmentation correlation. CONCLUSIONS: This study demonstrated the ability of KIM to locate the prostate with accuracy below other uncertainties in radiotherapy treatments, and the feasibility for KIM to be implemented across multiple institutions.

Real-time intrafraction motion monitoring in external beam radiotherapy.

Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT.

Dosimetric impact of intrafraction rotations in stereotactic prostate radiotherapy: A subset analysis of the TROG 15.01 SPARK trial.

BACKGROUND AND PURPOSE: Accurate delivery of radiotherapy is critical to achieve optimal treatment outcomes. Interfraction translational IGRT is now standard, and intrafraction motion management is becoming accessible. Some platforms can report both translational and rotational movements in real time. This study aims to quantify the dosimetric impact of observed intrafraction rotation of the prostate measured using monitoring software. MATERIALS AND METHODS: A dose grid resampling algorithm was used to model the dosimetric impact of prostate rotations for 20 patients on a SBRT prostate clinical trial. Translations were corrected before and during treatment, but rotations were not. Real time rotation data were acquired using KIM and a cumulative histogram analysis performed. Prostate volumes were rotated by the range of observed angles and used to calculate DVH data. RESULTS: The pitch axis had a higher range of observed rotations resulting in only 7 patients spending at least 90% of the beam on time across all fractions within rotation angles resulting in PTV D95% ≥36 Gy in this axis. The yaw and roll axes saw 17 and 15 patients respectively achieving this criterion. All but one of 20 patients exceeded CTV D98% ≥36 Gy for all observed rotation angles. CONCLUSIONS: Current CTV-PTV margins do not result in compromised CTV dose coverage due to inter and intrafraction prostate rotations in the absence of other uncertainties. Reduced PTV dosing is due to the extremely conformal treatment delivery but is unlikely to be clinically deleterious. Prostate standard IGRT should continue to focus on correcting any observed translational movements. Margin reduction could be explored in conjunction with other uncertainties.

A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.

PURPOSE: Real-time image-guided adaptive radiation therapy (IGART) requires accurate marker segmentation to resolve three-dimensional (3D) motion based on two-dimensional (2D) fluoroscopic images. Most common marker segmentation methods require prior knowledge of marker properties to construct a template. If marker properties are not known, an additional learning period is required to build the template which exposes the patient to an additional imaging dose. This work investigates a deep learning-based fiducial marker classifier for use in real-time IGART that requires no prior patient-specific data or additional learning periods. The proposed tracking system uses convolutional neural network (CNN) models to segment cylindrical and arbitrarily shaped fiducial markers. METHODS: The tracking system uses a tracking window approach to perform sliding window classification of each implanted marker. Three cylindrical marker training datasets were generated from phantom kilovoltage (kV) and patient intrafraction images with increasing levels of megavoltage (MV) scatter. The cylindrical shaped marker CNNs were validated on unseen kV fluoroscopic images from 12 fractions of 10 prostate cancer patients with implanted gold fiducials. For the training and validation of the arbitrarily shaped marker CNNs, cone beam computed tomography (CBCT) projection images from ten fractions of seven lung cancer patients with implanted coiled markers were used. The arbitrarily shaped marker CNNs were trained using three patients and the other four unseen patients were used for validation. The effects of full training using a compact CNN (four layers with learnable weights) and transfer learning using a pretrained CNN (AlexNet, eight layers with learnable weights) were analyzed. Each CNN was evaluated using a Precision-Recall curve (PRC), the area under the PRC plot (AUC), and by the calculation of sensitivity and specificity. The tracking system was assessed using the validation data and the accuracy was quantified by calculating the mean error, root-mean-square error (RMSE) and the 1st and 99th percentiles of the error. RESULTS: The fully trained CNN on the dataset with moderate noise levels had a sensitivity of 99.00% and specificity of 98.92%. Transfer learning of AlexNet resulted in a sensitivity and specificity of 99.42% and 98.13%, respectively, for the same datasets. For the arbitrarily shaped marker CNNs, the sensitivity was 98.58% and specificity was 98.97% for the fully trained CNN. The transfer learning CNN had a sensitivity and specificity of 98.49% and 99.56%, respectively. The CNNs were successfully incorporated into a multiple object tracking system for both cylindrical and arbitrarily shaped markers. The cylindrical shaped marker tracking had a mean RMSE of 1.6 ± 0.2 pixels and 1.3 ± 0.4 pixels in the x- and y-directions, respectively. The arbitrarily shaped marker tracking had a mean RMSE of 3.0 ± 0.5 pixels and 2.2 ± 0.4 pixels in the x- and y-directions, respectively. CONCLUSION: With deep learning CNNs, high classification performances on unseen patient images were achieved for both cylindrical and arbitrarily shaped markers. Furthermore, the application of CNN models to intrafraction monitoring was demonstrated using a simple tracking system. The results demonstrate that CNN models can be used to track markers without prior knowledge of the marker properties or an additional learning period.