MRI-guided Radiotherapy Research Articles

Structure-specific rigid dose accumulation dosimetric analysis of ablative stereotactic MRI-guided adaptive radiation therapy in ultracentral lung lesions.

Definitive local therapy with stereotactic ablative radiation therapy (SABR) for ultracentral lung lesions is associated with a high risk of toxicity, including treatment related death. Stereotactic MR-guided adaptive radiation therapy (SMART) can overcome many of the challenges associated with SABR treatment of ultracentral lesions. We retrospectively identified 14 consecutive patients who received SMART to ultracentral lung lesions from 10/2019 to 01/2021. Patients had a median distance from the proximal bronchial tree (PBT) of 0.38 cm. Tumors were most often lung primary (64.3%) and HILUS group A (85.7%). A structure-specific rigid registration approach was used for cumulative dose analysis. Kaplan-Meier log-rank analysis was used for clinical outcome data and the Wilcoxon Signed Rank test was used for dosimetric data. Here we show that SMART dosimetric improvements in favor of delivered plans over predicted non-adapted plans for PBT, with improvements in proximal bronchial tree DMax of 5.7 Gy (p = 0.002) and gross tumor 100% prescription coverage of 7.3% (p = 0.002). The mean estimated follow-up is 17.2 months and 2-year local control and local failure free survival rates are 92.9% and 85.7%, respectively. There are no grade ≥ 3 toxicities. SMART has dosimetric advantages and excellent clinical outcomes for ultracentral lung tumors. Daily plan adaptation reliably improves target coverage while simultaneously reducing doses to the proximal airways. These results further characterize the therapeutic window improvements for SMART. Structure-specific rigid dose accumulation dosimetric analysis provides insights that elucidate the dosimetric advantages of SMART more so than per fractional analysis alone.

Online Adaptive MR-Guided Ultrahypofractionated Radiotherapy of Prostate Cancer on a 1.5 T MR-Linac: Clinical Experience and Prospective Evaluation.

The use of hypofractionated radiotherapy in prostate cancer has been increasingly evaluated, whereas accumulated evidence demonstrates comparable oncologic outcomes and toxicity rates compared to normofractionated radiotherapy. In this prospective study, we evaluate all patients with intermediate-risk prostate cancer treated with ultrahypofractionated (UHF) MRI-guided radiotherapy on a 1.5 T MR-Linac within our department and report on workflow and feasibility, as well as physician-recorded and patient-reported longitudinal toxicity. A total of 23 patients with intermediate-risk prostate cancer treated on the 1.5 T MR-Linac with a dose of 42.7 Gy in seven fractions (seven MV step-and-shoot IMRT) were evaluated within the MRL-01 study (NCT04172753). The duration of each treatment step, choice of workflow (adapt to shape-ATS or adapt to position-ATP) and technical and/or patient-sided treatment failure were recorded for each fraction and patient. Acute and late toxicity were scored according to RTOG and CTC V4.0, as well as the use of patient-reported questionnaires. The median follow-up was 12.4 months. All patients completed the planned treatment. The mean duration of a treatment session was 38.2 min. In total, 165 radiotherapy fractions were delivered. ATS was performed in 150 fractions, 5 fractions were delivered using ATP, and 10 fractions were delivered using both ATS and ATP workflows. Severe acute bother (G3+) regarding IPS-score was reported in five patients (23%) at the end of radiotherapy. However, this tended to normalize and no G3+ IPS-score was observed later at any point during follow-up. Furthermore, no other severe genitourinary (GU) or gastrointestinal (GI) acute or late toxicity was observed. One-year biochemical-free recurrence survival was 100%. We report the excellent feasibility of UHF MR-guided radiotherapy for intermediate-risk prostate cancer patients and acceptable toxicity rates in our preliminary study. Randomized controlled studies with long-term follow-up are warranted to detect possible advantages over current state-of-the-art RT techniques.

1690: Comparing Transformer training strategies for real-time tumor tracking in MRI-guided radiotherapy

1690: Comparing Transformer training strategies for real-time tumor tracking in MRI-guided radiotherapy

905: Adaptive auto-segmentation for MRI-guided online adaptive radiotherapy of cervical carcinoma

905: Adaptive auto-segmentation for MRI-guided online adaptive radiotherapy of cervical carcinoma

MRI-guided Stereotactic Ablative Radiotherapy versus CT-guided Irreversible Electroporation in Advanced Pancreatic Cancer: Insights from the CROSSFIRE Trial.

MRI-guided Stereotactic Ablative Radiotherapy versus CT-guided Irreversible Electroporation in Advanced Pancreatic Cancer: Insights from the CROSSFIRE Trial.

3539: The use of MRI-guided palliative radiotherapy for metastatic spinal cord compression

3539: The use of MRI-guided palliative radiotherapy for metastatic spinal cord compression

3580: MRI-guided radiotherapy for prostate cancer and other pelvic tumours

3580: MRI-guided radiotherapy for prostate cancer and other pelvic tumours

3433: MRI-guided radiotherapy for head and neck cancer

3433: MRI-guided radiotherapy for head and neck cancer

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%/1mm to 3%/3mm. 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%/2mm, 1.35 ± 0.16% and 0.37 ± 0.15 in 2%/2mm, and 3.62 ± 0.55% and 0.32 ± 0.14 in 1%/1mm, respectively. The MAE and r of the APs were 1.13 ± 0.33% and 0.35 ± 0.22 in 3%/2mm, 1.68 ± 0.47% and 0.30 ± 0.11 in 2%/2mm, and 5.08 ± 0.29% and 0.15 ± 0.10 in 1%/1mm, respectively. The time cost was within 3s for the prediction. The results suggest the DL-based model has the potential for rapid GPR prediction in Elekta Unity.

Evaluation and mitigation of deformable image registration uncertainties for MRI-guided adaptive radiotherapy.

We evaluate the performance of a deformable image registration (DIR) software package in registering abdominal magnetic resonance images (MRIs) and then develop a mechanical modeling method to mitigate detected DIR uncertainties. Three evaluation metrics, namely mean displacement to agreement (MDA), DICE similarity coefficient (DSC), and standard deviation of Jacobian determinants (STD-JD), are used to assess the multi-modality (MM), contour-consistency (CC), and image-intensity (II)-based DIR algorithms in the MIM software package, as well as an in-house developed, contour matching-based finite element method (CM-FEM). Furthermore, we develop a hybrid FEM registration technique to modify the displacement vector field of each MIM registration. The MIM and FEM registrations were evaluated on MRIs obtained from 10 abdominal cancer patients. One-tailed Wilcoxon-Mann-Whitney (WMW) tests were conducted to compare the MIM registrations with their FEM modifications. For the registrations performed with the MIM-CC, MIM-MM, MIM-II, and CM-FEM algorithms, their average MDAs are 0.62±0.27, 2.39±1.30, 3.07±2.42, 1.04±0.72mm, and average DSCs are 0.94±0.03, 0.80±0.12, 0.77±0.15, 0.90±0.11, respectively. The p-values of the WMW tests between the MIM registrations and their FEM modifications are less than 0.0084 for STD-JDs and greater than 0.87 for MDA and DSC. Among the three MIM DIR algorithms, MIM-CC shows the smallest errors in terms of MDA and DSC but exhibits significant Jacobian uncertainties in the interior regions of abdominal organs. The hybrid FEM technique effectively mitigates the Jacobian uncertainties in these regions.

Super-resolution neural networks improve the spatiotemporal resolution of adaptive MRI-guided radiation therapy

BackgroundMagnetic resonance imaging (MRI) offers superb non-invasive, soft tissue imaging of the human body. However, extensive data sampling requirements severely restrict the spatiotemporal resolution achievable with MRI. This limits the modality’s utility in real-time guidance applications, particularly for the rapidly growing MRI-guided radiation therapy approach to cancer treatment. Recent advances in artificial intelligence (AI) could reduce the trade-off between the spatial and the temporal resolution of MRI, thus increasing the clinical utility of the imaging modality.MethodsWe trained deep learning-based super-resolution neural networks to increase the spatial resolution of real-time MRI. We developed a framework to integrate neural networks directly onto a 1.0 T MRI-linac enabling real-time super-resolution imaging. We integrated this framework with the targeting system of the MRI-linac to demonstrate real-time beam adaptation with super-resolution-based imaging. We tested the integrated system using large publicly available datasets, healthy volunteer imaging, phantom imaging, and beam tracking experiments using bicubic interpolation as a baseline comparison.ResultsDeep learning-based super-resolution increases the spatial resolution of real-time MRI across a variety of experiments, offering measured performance benefits compared to bicubic interpolation. The temporal resolution is not compromised as measured by a real-time adaptation latency experiment. These two effects, an increase in the spatial resolution with a negligible decrease in the temporal resolution, leads to a net increase in the spatiotemporal resolution.ConclusionsDeployed super-resolution neural networks can increase the spatiotemporal resolution of real-time MRI. This has applications to domains such as MRI-guided radiation therapy and interventional procedures.

Narrative review of the utility of magnetic resonance imaging in radiotherapy for cervical cancer.

In radiotherapy (RT) for locally advanced cervical cancer, high soft tissue contrast on magnetic resonance imaging (MRI) can ensure accurate delineation of target volumes (TVs) and optimal dose distribution to the RT target and organs at risk (OAR). MRI-guided adaptive RT (MRIgART) is a novel technology that revises RT plans according to anatomical changes occurring throughout the treatment to improve target coverage and minimise OAR toxicity. This review aims to assess the evidence and gaps of MRI use in RT planning and MRIgART in the treatment of cervical cancer, as well as challenges in its clinical implementation. Ovid Medline and PubMed were searched using keywords for MRI in RT for cervical cancer. After applying the inclusion and exclusion criteria, the initial search was deduced to 32 studies. A total of 37 final studies were reviewed, including eight additional articles from references. In the primary studies, TVs and organ motion were assessed before, during, and after treatment. MRI was used to investigate dose distribution and therapeutic response to the treatment in association with its outcome. Lastly, rationales for MRIgART were evaluated. It was concluded that MRI enables accurate target delineation, assessment of organ motion and interfraction changes, and monitoring of treatment response through dynamic parameters. Enhanced target coverage and reduced OAR irradiation through MRIgART can improve local control and the overall outcome, although its rationales against the logistical challenges need to be evaluated on further research.

Radiomodulating Properties of Superparamagnetic Iron Oxide Nanoparticle (SPION) Agent Ferumoxytol on Human Monocytes: Implications for MRI-Guided Liver Radiotherapy.

Superparamagnetic iron oxide nanoparticles (SPION) have attracted great attention not only for therapeutic applications but also as an alternative magnetic resonance imaging (MRI) contrast agent that helps visualize liver tumors during MRI-guided stereotactic body radiotherapy (SBRT). SPION can provide functional imaging of liver parenchyma based upon its uptake by the hepatic resident macrophages or Kupffer cells with a relative enhancement of malignant tumors that lack Kupffer cells. However, the radiomodulating properties of SPION on liver macrophages are not known. Utilizing human monocytic THP-1 undifferentiated and differentiated cells, we characterized the effect of ferumoxytol (Feraheme®), a carbohydrate-coated ultrasmall SPION agent at clinically relevant concentration and therapeutically relevant doses of gamma radiation on cultured cells in vitro. We showed that ferumoxytol affected both monocytes and macrophages, increased the resistance of monocytes to radiation-induced cell death and inhibition of cell activity, and supported the anti-inflammatory phenotype of human macrophages under radiation. Its effect on human cells depended on the duration of SPION uptake and was radiation dose-dependent. The results of this pilot study support a strong mechanism-based optimization of SPION-enhanced MRI-guided liver SBRT for primary and metastatic liver tumors, especially in patients with liver cirrhosis awaiting a liver transplant.

MRI-guided stereotactic ablative body radiotherapy versus CT-guided percutaneous irreversible electroporation for locally advanced pancreatic cancer (CROSSFIRE): a single-centre, open-label, randomised phase 2 trial

Pancreatic ductal adenocarcinoma is an aggressive disease with a dismal prognosis. Stage III locally advanced pancreatic cancer is considered unresectable and current palliative chemotherapy regimens only modestly improve survival. Guidelines suggest chemoradiation or stereotactic ablative body radiotherapy (SABR) could be beneficial in certain circumstances. Other local treatments such as irreversible electroporation could enhance patient outcomes by extending survival while preserving quality of life. We aimed to compare the efficacy and safety of MRI-guided SABR versus CT-guided percutaneous irreversible electroporation following standard FOLFIRINOX chemotherapy. CROSSFIRE was an open-label, randomised phase 2 superiority trial conducted at the Amsterdam University Medical Centre (Amsterdam, Netherlands). Eligible patients were aged 18 years or older with confirmed histological and radiological stage III locally advanced pancreatic cancer. The maximum tumour diameter was 5 cm and patients had to be pretreated with three to eight cycles of FOLFIRINOX. Patients were randomly assigned (1:1) to MRI-guided SABR (five fractions of 8 Gy delivered on non-consecutive days) or CT-guided percutaneous irreversible electroporation using a computer-generated variable block randomisation model. The primary endpoint was overall survival from randomisation, assessed in the intention-to-treat population. Safety was assessed in the per-protocol population. A prespecified interim futility analysis was done after inclusion of half the original sample size, with a conditional probability of less than 0·2 resulting in halting of the study. The trial was registered at ClinicalTrials.gov, NCT02791503. Between May 1, 2016, and March 31, 2022, 68 patients were enrolled and randomly assigned to SABR (n=34) or irreversible electroporation (n=34), of whom 64 were treated according to protocol. Of the 68 participants, 36 (53%) were male and 32 (47%) were female, with a median age of 65 years (IQR 57-70). Median overall survival from randomisation was 16·1 months (95% CI 12·1-19·4) in the SABR group versus 12·5 months (10·9-17·0) in the irreversible electroporation group (hazard ratio [HR] 1·39 [95% CI 0·84-2·30]; p=0·21). The conditional probability to demonstrate superiority of either technique was 0·13; patient accrual was therefore stopped early for futility. 20 (63%) of 32 patients in the SABR group versus 19 (59%) of 32 patients in the irreversible electroporation group had adverse events (p=0·8) and five (16%) patients in the SABR group versus eight (25%) in the irreversible electroporation group had grade 3-5 adverse events (p=0·35). The most common grade 3-4 adverse events were cholangitis (two [6%] in the SABR group vs one [3%] in the irreversible electroporation group), abdominal pain (one [3%] vs two [6%]), and pancreatitis (none vs two [6%]). One (3%) patient in the SABR group and one (3%) in the irreversible electroporation group died from a treatment-related adverse event. CROSSFIRE did not identify a difference in overall survival or incidence of adverse events between MRI-guided SABR and CT-guided percutaneous irreversible electroporation after FOLFIRINOX. Future studies should further assess the added value of local ablative treatment over chemotherapy alone. Adessium Foundation, AngioDynamics.

Novel Virtual Reality App for Training Patients on MRI-guided Radiation Therapy

PurposePatients receiving respiratory gated magnetic resonance imaging-guided radiation therapy (MRIgRT) for abdominal targets must hold their breath for ≥25 seconds at a time. Virtual reality (VR) has shown promise for improving patient education and experience for diagnostic MRI scan acquisition. We aimed to develop and pilot-test the first VR app to educate, train, and reduce anxiety and discomfort in patients preparing to receive MRIgRT. Methods and MaterialsA multidisciplinary team iteratively developed a new VR app with patient input. The app begins with minigames to help orient patients to using the VR device and to train patients on breath-holding. Next, app users are introduced to the MRI linear accelerator vault and practice breath-holding during MRIgRT. In this quality improvement project, clinic personnel and MRIgRT-eligible patients with pancreatic cancer tested the VR app for feasibility, acceptability, and potential efficacy for training patients on using breath-holding during MRIgRT. ResultsThe new VR app experience was tested by 19 patients and 67 clinic personnel. The experience was completed on average in 18.6 minutes (SD = 5.4) by patients and in 14.9 (SD = 3.5) minutes by clinic personnel. Patients reported the app was “extremely helpful” (58%) or “very helpful” (32%) for learning breath-holding used in MRIgRT and “extremely helpful” (28%) or “very helpful (50%) for reducing anxiety. Patients and clinic personnel also provided qualitative feedback on improving future versions of the VR app. ConclusionThe VR app was feasible and acceptable for training patients on breath-holding for MRIgRT. Patients eligible for MRIgRT for pancreatic cancer and clinic personnel reported on future improvements to the app to enhance its usability and efficacy.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy.

Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT)workflow. In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostatecancer. Three-dimensional pelvic T2-weighted MRIs of 143 prostate cancer patients undergoing radiotherapy were collected and divided into 110, 13, and 20 patients for training, validation, and testing. We designed a framework using convolutional neural networks (CNNs) for rigid and deformable registration. We selected the deformable registration network architecture among U-Net, MS-D Net, and LapIRN and optimized the training strategy (end-to-end vs. sequential). The framework was compared against an iterative baseline registration. We evaluated registration accuracy (the Dice and Hausdorff distance of the prostate and bladder contours), structural similarity index, and folding percentage to compare the methods. We also evaluated the framework's robustness to rigid and elastic deformations and bias fieldperturbations. The end-to-end trained framework comprising LapIRN for the deformable component achieved the best median (interquartile range) prostate and bladder Dice of0.89 (0.85-0.91) and 0.86 (0.80-0.91), respectively. This accuracy was comparable to the iterative baseline registration: prostate and bladder Dice of 0.91 (0.88-0.93) and 0.86 (0.80-0.92). The best models complete rigid and deformable registration in 0.002 (0.0005) and 0.74 (0.43) s (Nvidia Tesla V100-PCIe 32 GB GPU), respectively. We found that the models are robust to translations up to 52 mm, rotations up to 15 , elastic deformations up to 40 mm, and biasfields. Our proposed unsupervised, deep learning-based registration framework can perform rigid and deformable registration in less than a second with contour propagation accuracy comparable with iterative registration.

Technical note: Minimizing CIED artifacts on a 0.35 T MRI-Linac using deep learning.

Artifacts from implantable cardioverter defibrillators (ICDs) are a challenge to magnetic resonance imaging (MRI)-guided radiotherapy (MRgRT). This study tested an unsupervised generative adversarial network to mitigate ICD artifacts in balanced steady-state free precession (bSSFP) cine MRIs and improve image quality and tracking performance for MRgRT. Fourteen healthy volunteers (Group A) were scanned on a 0.35 T MRI-Linac with and without an MR conditional ICD taped to their left pectoral to simulate an implanted ICD. bSSFP MRI data from 12 of the volunteers were used to train a CycleGAN model to reduce ICD artifacts. The data from the remaining two volunteers were used for testing. In addition, the dataset was reorganized three times using a Leave-One-Out scheme. Tracking metrics [Dice similarity coefficient (DSC), target registration error (TRE), and 95 percentile Hausdorff distance (95% HD)] were evaluated for whole-heart contours. Image quality metrics [normalized root mean square error (nRMSE), peak signal-to-noise ratio (PSNR), and multiscale structural similarity (MS-SSIM) scores] were evaluated. The technique was also tested qualitatively on three additional ICD datasets (Group B) including a patient with an implanted ICD. For the whole-heart contour with CycleGAN reconstruction: 1) Mean DSC rose from 0.910 to 0.935; 2) Mean TRE dropped from 4.488 to 2.877mm; and 3) Mean 95% HD dropped from 10.236 to 7.700mm. For the whole-body slice with CycleGAN reconstruction: 1) Mean nRMSE dropped from 0.644 to 0.420; 2) Mean MS-SSIM rose from 0.779 to 0.819; and 3) Mean PSNR rose from 18.744 to 22.368. The three Group B datasets evaluated qualitatively displayed a reduction in ICD artifacts in the heart. CycleGAN-generated reconstructions significantly improved both tracking and image quality metrics when used to mitigate artifacts from ICDs.

Evaluation of clinical parallel workflow in online adaptive MR-guided Radiotherapy: A detailed assessment of treatment session times

IntroductionAdvancements in MRI-guided radiotherapy (MRgRT) enable clinical parallel workflows (CPW) for online adaptive planning (oART), allowing medical physicists (MPs), physicians (MDs), and radiation therapists (RTTs) to perform their tasks simultaneously. This study evaluates the impact of this upgrade on the total treatment time by analyzing each step of the current 0.35T-MRgRT workflow. MethodsThe time process of the workflow steps for 254 treatment fractions in 0.35 MRgRT was examined. Patients have been grouped based on disease site, breathing modality (BM) (BHI or FB), and fractionation (stereotactic body RT [SBRT] or standard fractionated long course [LC]). The time spent for the following workflow steps in Adaptive Treatment (ADP) was analyzed: Patient Setup Time (PSt), MRI Acquisition and Matching (MRt), MR Re-contouring Time (RCt), Re-Planning Time (RPt), Treatment Delivery Time (TDt). Also analyzed was the timing of treatments that followed a Simple workflow (SMP), without the online re-planning (PSt + MRt + TDt.). ResultsThe time analysis revealed that the ADP workflow (median: 34 min) is significantly (p < 0.05) longer than the SMP workflow (19 min). The time required for ADP treatments is significantly influenced by TDt, constituting 40 % of the total time. The oART steps (RCt + RPt) took 11 min (median), representing 27 % of the entire procedure. Overall, 79.2 % of oART fractions were completed in less than 45 min, and 30.6 % were completed in less than 30 min. ConclusionThis preliminary analysis, along with the comparative assessment against existing literature, underscores the potential of CPW to diminish the overall treatment duration in MRgRT-oART. Additionally, it suggests the potential for CPW to promote a more integrated multidisciplinary approach in the execution of oART.

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

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

MRI-guided radiotherapy in twenty fractions for localised prostate cancer; results from the MOMENTUM study

Background and purposeMRI-guided radiotherapy (MRIgRT) offers multiple potential advantages over CT-guidance. This study examines the potential clinical benefits of MRIgRT for men with localised prostate cancer, in the setting of moderately hypofractionated radiotherapy. We evaluate two-year toxicity outcomes, early biochemical response and patient-reported outcomes (PRO), using data obtained from a multicentre international registry study, for the first group of patients with prostate cancer who underwent treatment on a 1.5 T MR-Linac. Materials and methodsPatients who were enrolled within the MOMENTUM study and received radical treatment with 60 Gy in 20 fractions were identified. PSA levels and CTCAE version 5.0 toxicity data were measured at follow-up visits. Those patients who consented to PRO data collection also completed EQ-5D-5L, EORTC QLQ-C30 and EORTC QLQ-PR25 questionnaires. ResultsBetween November 2018 and June 2022, 146 patients who had MRIgRT for localised prostate cancer on the 1.5 T MR-Linac were eligible for this study. Grade 2 and worse gastro-intestinal (GI) toxicity was reported in 3 % of patients at three months whilst grade 2 and worse genitourinary (GU) toxicity was 7 % at three months. There was a significant decrease in the median PSA at 12 months. The results from both the EQ-5D-5L data and EORTC global health status scale indicate a decline in the quality of life (QoL) during the first six months. The mean change in score for the EORTC scale showed a decrease of 11.4 points, which is considered clinically important. QoL improved back to baseline by 24 months. Worsening of hormonal symptoms in the first six months was reported with a return to baseline by 24 months and sexual activity in all men worsened in the first three months and returned to baseline at 12 months. ConclusionThis study establishes the feasibility of online-MRIgRT for localised prostate on a 1.5 T MR-Linac with low rates of toxicity, similar to that published in the literature. However, the clinical benefits of MRIgRT over conventional radiotherapy in the setting of moderate hypofractionation is not evident. Further research will focus on the delivery of ultrahypofractionated regimens, where the potential advantages of MRIgRT for prostate cancer may become more discernible.