MRI-guided Radiotherapy Research Articles

Outcomes of simultaneous, adaptive, MRI-guided liver SBRT in patients with heavily pretreated colorectal cancer and four or more liver metastases.

160 Background: About one-third of colorectal cancer (CRC) patients (pts) develop metastases, most commonly in the liver (CRLM). Ablative treatment of multiple liver lesions is difficult and often not done with conventional CT-based radiation therapy, but feasible with MRI-guided adaptive radiation. This study reviews our experience treating pts with high burden CRLM using MRI-guided stereotactic body radiation therapy (MRgRT). Methods: This study identified pts with 4 or more CRLM treated with MRgRT between 7/2019 and 4/2024, all of whom had progressive disease during or after standard-of-care therapy. Ablative radiation was delivered to all sites of disease with no concurrent systemic therapy. All with Child-Pugh score 5-6 (class A) liver function. Local control was defined as no progression in any treated lesion on a per-patient basis. Descriptive statistics summarized the data. A Cox proportional hazards model was used to identify predictors of survival, while the Kaplan-Meier method was used to estimate survival functions. Statistical analysis was performed using SPSS. Results: Seventeen pts with 110 lesions were included, with a median follow-up of 10.7 months (range, 1.7–36.9). The majority (n=15) received systemic therapy in the year before MRgRT. The median number of prior lines of systemic therapy was 2 (range, 1–5). Eight pts (47.1%) had nodal and/or lung progression in addition to CRLM, and 4 pts (23.5%) had at least one prior course of MRgRT. A median of 6 liver lesions (range, 4–11) were prescribed a median dose of 60 Gy (range, 60–80), each delivered in 5 fractions. Median total gross tumor volume per pt was 23.8 cm 3 (range, 0.97–60.1). Seventy-four fractions were available for radiation delivery analysis, of which 55 (74.3%) were adapted. At least one target was modified in 51 (92.7%) of the adapted fractions. Acute toxicity occurred in 7 pts, all with self-limited fatigue and nausea. Progression in Child-Pugh classification from A to B occurred in three pts 3-6 months post-MRgRT. Late toxicity occurred in 1 patient with mild, possibly radiation-related peripheral biliary dilation. Two pts developed malignant biliary strictures. Per-patient local control was 50.8% and 34.8% at 1 and 2 years, respectively, while liver control was 28.7% at 2 years. Six-month systemic therapy-free survival was 31.9%. Two-year overall survival (OS) was 43.9%, with a median of 17.1 months (range, 8.6–25.7). The size of the largest tumor correlated with OS (HR 1.23, p=0.014). Conclusions: In this small series of CRC pts with 4+ CRLM, MRgRT is well-tolerated and shows promise as astrategy to consolidate disease and delay the immediate need for systemic therapy changes. Patient selection is critical to identify those most likely to benefit, though the overall impact on survival and disease trajectory remains uncertain.

Correlation of mid-chemoradiation ctDNA results and clinical complete response to total neoadjuvant therapy (TNT) for locally advanced rectal adenocarcinoma.

263 Background: The total neoadjuvant therapy (TNT) paradigm for locally advanced rectal cancer (LARC) is evolving with intensification and de-escalation neoadjuvant therapies. Chemoradiation (CRT) followed by consolidation chemotherapy offers the highest rate of organ preservation. Circulating tumor DNA (ctDNA) response during TNT may serve as a biomarker that allows for therapy personalization to better select candidates for rectal organ preservation (Watch-and-Wait, WW). Methods: We enrolled patients with mismatch repair proficient LARC eligible for TNT on a prospective protocol (NCT05108428). Patients underwent CRT to 50.4 Gy with a mid-treatment evaluation (27 Gy) on a MRI-linac followed by 8 cycles of consolidation FOLFOX. A sequential MRI-guided adaptive radiotherapy boost to 59.4 Gy was delivered if rectal tumor volume reduced > 30% at 27 Gy. Standard restaging with diagnostic MRI and endoscopy occurred after TNT to evaluate organ preservation candidacy. ctDNA was obtained at diagnosis, mid-CRT (27 Gy), completion of TNT, and regular intervals in surveillance. ctDNA analysis was performed using a clinically validated, personalized, tumor-informed assay (Signatera, Natera, Inc.). Descriptive statistics for clinical response for mid-treatment assessment are reported. Additional clinical outcomes will be reported as follow up matures. Results: A total of 20 patients were enrolled (70% male, 70% cN+) with average age of 58 (range: 30-86) and 40% with threatened or involved radial margin. Average follow up is 12 months (range: 4-24 months) from date of completion of TNT. Average initial tumor volume was 26cc (range: 8.6cc- 66.8cc) with an average tumor reduction of 68.5% at 27 Gy. Volumetric change based on mid treatment MRI >30% did not predict WW eligibility (2 patients with <30% volumetric change maintains clinical complete responses). All patients demonstrated positive ctDNA at diagnosis (average 7.08 MTM/mL, range: 0.08 MTM/mL -77.96 MTM/mL). 25% of patients had elevated CEA at diagnosis. Conversion to negative ctDNA at mid-CRT occurred in 53% of patients. 57% of patients with a positive ctDNA at this timepoint underwent formal oncologic mesorectal excision with adenocarcinoma confirmed in the specimens. 25% of patients with negative ctDNA at this timepoint underwent a local excision, with no histologic invasive cancer in both specimens. We observed a 100% rectal organ preservation rate when early conversion to negative ctDNA at the 27 Gy of CRT timepoint. Conclusions: ctDNA clearance during CRT may be a good early predictor for patients that may be a candidate for a WW protocol. This biomarker may also guide consolidation therapy escalation (FOLFIRINOX) or de-escalation (chemotherapy omission) for select patients. Longer follow up is needed to correlate with clinical outcomes and future studies with more patients is needed. Clinical trial information: NCT05108428 .

Deep learning-based quick MLC sequencing for MRI-guided online adaptive radiotherapy: a feasibility study for pancreatic cancer patients.

One bottleneck of MRI-guided Online Adaptive Radiotherapy (MRoART) is the time-consuming daily online replanning process. The current leaf sequencing method takes up to 10 minutes, with potential dosimetric degradation and small segment openings that increase delivery time. This work aims to replace this process with a fast deep learning-based method to provide deliverable MLC sequences almost instantaneously, potentially accelerating and enhancing online adaption.
Approach: Daily MRIs and plans from 242 daily fractions of 49 abdomen cancer patients on a 1.5T MR-Linac were used. The architecture included: 1) a recurrent conditional Generative Adversarial Network (rcGAN) model to predict segment shapes from a fluence map (FM), recurrently predicting each segment's shape; and 2) a linear matrix equation module to optimize the monitor units (MU) weights of segments. Multiple models with different segment numbers per beam (4-7) were trained. The final MLC sequences with the smallest relative absolute errors were selected. The predicted MLC sequence was imported into treatment planning system for dose calculation and compared with the original plans.
Main results: The gamma passing rate for all fractions was 99.7±0.2% (2%/2mm criteria) and 92.7±1.6% (1%/1mm criteria). The average number of segments per beam in the proposed method was 6.0±0.6 compared to 7.5 ± 0.3 in the original plan. The average total MUs were reduced from 1641 ± 262 to 1569.5 ± 236.7 in the predicted plans. The estimated delivery time was reduced from 9.7 minutes to 8.3 minutes, an average reduction of 14% and up to 25% for individual plans. Execution time for one plan was less than 10 seconds using a GTX1660TIGPU.
Significance: The developed models can quickly and accurately generate an optimized, deliverable leaf sequence from a FM with fewer segments. This can seamlessly integrate into the current online replanning workflow, greatly expediting the daily plan adaptation process.
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A robust auto-contouring and data augmentation pipeline for adaptive MRI-guided radiotherapy of pancreatic cancer with a limited dataset

Objective.This study aims to develop and evaluate a fast and robust deep learning-based auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets.Approach.This study was conducted in two phases. In phase I, we selected and customized the best-performing segmentation model among ResU-Net, SegResNet, and nnU-Net, using 43 balanced 3DVane images from 10 patients with 5-fold cross-validation. Phase II focused on optimizing the chosen model through two advanced data augmentation approaches to improve performance and generalizability by increasing the effective input dataset: (1) a novel structure-guided deformation-based augmentation approach (sgDefAug) and (2) a generative adversarial network-based method using a cycleGAN (GANAug). These were compared with comprehensive conventional augmentations (ConvAug). The approaches were evaluated using geometric (Dice score, average surface distance (ASD)) and dosimetric (D2% and D50% from dose-volume histograms) criteria.Main results.The nnU-Net framework demonstrated superior performance (mean Dice: 0.78 ± 0.10, mean ASD: 3.92 ± 1.94 mm) compared to other models. The sgDefAug and GANAug approaches significantly improved model performance over ConvAug, with sgDefAug demonstrating slightly superior results (mean Dice: 0.84 ± 0.09, mean ASD: 3.14 ± 1.79 mm). The proposed methodology produced auto-contours in under 30 s, with 75% of organs showing less than 1% difference in D2% and D50% dose criteria compared to ground truth.Significance.The integration of the nnU-Net framework with our proposed novel augmentation technique effectively addresses the challenges of limited datasets and stringent time constraints in online adaptive radiotherapy for pancreatic cancer. Our approach offers a promising solution for streamlining online adaptive workflows and represents a substantial step forward in the practical application of auto-segmentation techniques in clinical radiotherapy settings.

Non-medicinal oral contrast in upper abdominal MRI for MR-guided radiotherapy: A scoping review.

Using non-medicinal oral contrast agents may aid safe delivery of magnetic resonance image-guided (MR-guided) radiotherapy by improving the ability to visualise and avoid excessive radiation dose to adjacent bowel/stomach. This scoping review aims to map the literature on non-medicinal oral contrasts used in upper-abdominal diagnostic or therapeutic magnetic resonance imaging (MRI) to find potential candidates for employing in MR-guided radiotherapy and identify gaps in knowledge for further study. A scoping review of non-medicinal oral contrast used in upper-abdominal MRI research followed a pre-defined protocol based on Arksey and O'Malley's framework. Data were charted and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses for Scoping Reviews reporting guidelines. Forty-seven studies from 1955 screened abstracts were charted. Thirty-one distinct non-medicinal oral contrast were identified, used primarily to enhance tissue visualisation (89%) or observe motility (11%) in diagnostic studies. All studies reported to be predominantly quantitative; only 13% included participant experience via questionnaires and none used qualitative methods. No studies have examined the efficacy of non-medicinal oral contrasts in MR-guided radiotherapy planning or delivery. Non-medicinal oral contrasts have been extensively investigated in diagnostic MRI to enhance gastrointestinal visualisation and assess motility. However, non-medicinal oral contrasts have not been investigated in the context of radiotherapy planning and treatment. Qualitative evaluation of the patient experience of non-medicinal oral contrasts in magnetic resonance image-guided radiotherapy should be considered alongside studies quantifying the potential clinical benefit. This review summarises the properties of non-medicinal oral contrasts and identifies critical gaps in the current evidence, particularly the absence of qualitative research in this domain and the unexplored potential for their application in MR-guided radiotherapy planning and delivery.

Automatic segmentation of MRI images for brain radiotherapy planning using deep ensemble learning

Backgroundand Purpose:This study aimed to develop and evaluate an efficient method to automatically segment T1- and T2-weighted brain magnetic resonance imaging (MRI) images. We specifically compared the segmentation performance of individual convolutional neural network (CNN) models against an ensemble approach to advance the accuracy of MRI-guided radiotherapy (RT) planning.Materials and Methods. The evaluation was conducted on a private clinical dataset and a publicly available dataset (HaN-Seg). Anonymized MRI data from 55 brain cancer patients, including T1-weighted, T1-weighted with contrast, and T2-weighted images, were used in the clinical dataset. We employed an EDL strategy that integrated five independently trained 2D neural networks, each tailored for precise segmentation of tumors and organs at risk (OARs) in the MRI scans. Class probabilities were obtained by averaging the final layer activations (Softmax outputs) from the five networks using a weighted-average method, which were then converted into discrete labels. Segmentation performance was evaluated using the Dice similarity coefficient (DSC) and Hausdorff distance at 95% (HD95). The EDL model was also tested on the HaN-Seg public dataset for comparison.Results. The EDL model demonstrated superior segmentation performance on both the clinical and public datasets. For the clinical dataset, the ensemble approach achieved an average DSC of 0.7 ± 0.2 and HD95 of 4.5 ± 2.5 mm across all segmentations, significantly outperforming individual networks which yielded DSC values ≤0.6 and HD95 values ≥14 mm. Similar improvements were observed in the HaN-Seg public dataset.Conclusions. Our study shows that the EDL model consistently outperforms individual CNN networks in both clinical and public datasets, demonstrating the potential of ensemble learning to enhance segmentation accuracy. These findings underscore the value of the EDL approach for clinical applications, particularly in MRI-guided RT planning.

Prostate motion in magnetic resonance imaging-guided radiotherapy and its impact on margins.

This study focused on reducing the margin for prostate cancer treatment using magnetic resonance imaging-guided radiotherapy by investigating the intrafractional motion of the prostate and different motion-mitigation strategies. We retrospectively analyzed intrafractional prostate motion in 77patients with low- to intermediate-risk prostate cancer treated with five fractions of 7.25 Gy on a1.5 T magnetic resonance linear accelerator. Systematic drift motion was observed and described by an intrafractional motion model. The planning target volume (PTV) margin was calculated in acohort of 77patients and prospectively evaluated for geometric coverage in aseparate cohort of 24patients. The intrafractional model showed that the prostate position starts out of equilibrium for the anterior-posterior (-1.8 ± 3.1mm) and superior-inferior (1.7 ± 2.6mm) directions, with relaxation times of12 and 15min, respectively. Position verification scans are acquired at 30min on average. At that time, the transient drift motion becomes indistinguishable from the residual random intrafractional motion. PTV margins can be reduced to 1.8 mm (left-right), 3.2 mm (anterior-posterior), and 2.9 mm (superior-inferior). Evaluation of the overlap with the clinical target volume (CTV) was performed for atotal of 120 fractions of 24patients. The overlap range between the CTV and the PTV was 93-100% and the applied 3‑mm PTV margin for the CTV had a99.5% averaged geometric overlap for all patients. APTV margin reduction to 3 mm is feasible. Apatient-specific approach could reduce the margins further.

Joint k-b space diffusion-weighted image reconstruction and apparent diffusion coefficient fitting for diffusion-weighted turbo-spin-echo imaging.

Diffusion-weighted (DW) turbo-spin-echo (TSE) imaging offers improved geometric fidelity compared to single-shot echo-planar-imaging (EPI). However, it suffers from low signal-to-noise ratio (SNR) and prolonged acquisition times, thereby restricting its applications in diagnosis and MRI-guided radiotherapy (MRgRT). To develop a joint k-b space reconstruction algorithm for concurrent reconstruction of DW-TSE images and the apparent diffusion coefficient (ADC) map with enhanced image quality and more accurate quantitative measurements. The joint k-b reconstruction model was formulated as an optimization problem subject to a self-consistency condition comprising the exponential decay relationship between DW images and the ADC map. The objective function included a data fidelity term confirming an agreement between the reconstructed images and measured k-space data, incorporating the mono-exponential decay relationship between DW images and ADC map as a constraint, along with spatial regularization terms on both amplitude and phase images. The optimization problem was solved using the alternating-direction method of multipliers (ADMM). We evaluated the performance of the joint reconstruction (JR) algorithm for DW-TSE in a phantom study and patient studies on five brain and head/neck cancer patients. Image distortion, accuracy, and repeatability of ADC measurements were assessed and compared with those from conventional Fourier-transformed (FFT)-based reconstruction and magnitude-based ADC fitting methods, as well as with the clinically used DW-EPI technique. The proposed joint k-b reconstruction method demonstrated improved SNR and enhanced accuracy of ADC measurements in DW-TSE compared to the conventional method. Additionally, JR-DW-TSE with fewer averages at high b-values provided better image quality than the conventional FFT-reconstructed DW-TSE with full averages. The proposed joint k-b reconstruction method for DW-TSE has the potential to deliver distortion-robust diffusion-weighted MRI (DWI) for clinical applications, which is particularly crucial for MRgRT.

Fluorescence molecular imaging-guided photodynamic therapy for early breast cancer in the prone position: feasibility evaluation with Monte Carlo simulations.

The successful diagnosis and treatment of early-stage breast cancer enhances the quality of life of patients. As a promising alternative to recently developed magnetic resonance imaging-guided radiotherapy, we proposed fluorescence molecular imaging-guided photodynamic therapy (FMI-guided PDT), which requires no expensive equipment. In the FMI simulations, ICG-C11 which has emission peaks at near-infrared wavelengths was used as the FMI agent. In the PDT simulation, Upconversion nanoparticles-Quantum dots-Rose bengal (UCQR) which was a PDT agent with upconversion capabilities was used. The feasibility of breast cancer diagnosis and treatment using our proposed method is evaluated through Monte Carlo simulations of exact light transport through a realistic breast model in the prone position. Monte Carlo modeling in voxelized media was performed. Fluorescence propagation from the tumor and the amount of singlet oxygen produced within the tumor were estimated from the calculated fluence. Next, the effects of tumor diameter and depth from the skin surface on the simulation results were evaluated. The simulation results showed successful detection of tumors with diameters of 5-9 mm in the 15-25 mm depth region, where tumors are commonly found. Furthermore, simulations have estimated that those tumors can be completely treated by PDT with less than ten light irradiations. This study suggests that fluorescent molecular imaging-guided photodynamic therapy may be a potential treatment for early-stage breast cancer. Our method would be more suitable than the conventional method for young women who are at higher risk of radiation exposure effects.

MRgRT real-time target localization using foundation models for contour point tracking and promptable mask refinement.

This study aimed to evaluate two real-time target tracking approaches for magnetic resonance imaging (MRI) guided radiotherapy (MRgRT) based on foundation artificial intelligence (AI) models.

Approach: The first approach used a point-tracking model that propagates points from a reference contour. The second approach used a video-object-segmentation model, based on Segment Anything Model 2 (SAM2). Both approaches were evaluated and compared against each other, inter-observer variability, and a transformer-based image registration model, TransMorph, with and without patient-specific (PS) fine-tuning. The evaluation was carried out on 2D cine MRI datasets from two institutions, containing scans from 33 patients with 8060 labeled frames, with annotations from 2 to 5 observers per frame, totaling 29179 ground truth segmentations. The segmentations produced were assessed using the Dice similarity coefficient (DSC), 50% and 95% Hausdorff distances (HD50 / HD95), and the Euclidean center distance (ECD).

Main results: The results showed that the contour tracking (median DSC 0.92 ± 0.04 and ECD 1.9 ± 1.0 mm) and SAM2-based (median DSC 0.93 ± 0.03 and ECD 1.6 ± 1.1 mm) approaches produced target segmentations comparable or superior to TransMorph without PS fine-tuning (median DSC 0.91 ± 0.07 and ECD 2.6 ± 1.4 mm) and slightly inferior to TransMorph with PS fine-tuning (median DSC 0.94 ± 0.03 and ECD 1.4 ± 0.8 mm). Between the two novel approaches, the one based on SAM2 performed marginally better at a higher computational cost (inference times 92 ms for contour tracking and 109 ms for SAM2). Both approaches and TransMorph with PS fine-tuning exceeded inter-observer variability (median DSC 0.90 ± 0.06 and ECD 1.7 ± 0.7 mm).

Significance: This study demonstrates the potential of foundation models to achieve high-quality real-time target tracking in MRgRT, offering performance that matches state-of-the-art methods without requiring PS fine-tuning.

Real-time 3D MR guided radiation therapy through orthogonal MR imaging and manifold learning.

In magnetic resonance image (MRI)-guided radiotherapy (MRgRT), 2D rapid imaging is commonly used to track moving targets with high temporal frequency to minimize gating latency. However, anatomical motion is not constrained to 2D, and a portion of the target may be missed during treatment if 3D motion is not evaluated. While some MRgRT systems attempt to capture 3D motion by sequentially tracking motion in 2D orthogonal imaging planes, this approach assesses 3D motion via independent 2D measurements at alternating instances, lacking a simultaneous 3D motion assessment in both imaging planes. We hypothesized that a motion model could be derived from prior 2D orthogonal imaging to estimate 3D motion in both planes simultaneously. We present a manifold learning technique to estimate 3D motion from 2D orthogonal imaging. Five healthy volunteers were scanned under an IRB-approved protocol using a 3.0 T Siemens Skyra simulator. Images of the liver dome were acquired during free breathing (FB) with a 2.6mm × 2.6mm in-plane resolution for approximately 10min in alternating sagittal and coronal planes at ∼5 frames per second. The motion model was derived using a combined manifold learning and alignment approach based on locally linear embedding (LLE). The model utilized the spatially overlapping MRI signal shared by both imaging planes to group together images that had similar signals, enabling motion estimation in both planes simultaneously. The model's motion estimates were compared to the ground truth motion derived in each newly acquired image using deformable registration. A simulated target was defined on the dome of the liver and used to evaluate model performance. The Dice similarity coefficient and distance between the model-tracked and image-tracked contour centroids were evaluated. Motion modeling error was estimated in the orthogonal plane by back-propagating the motion to the currently imaged plane and by interpolating the motion between image acquisitions where ground truth motion was available. The motion observed in the healthy volunteer studies ranged from 12.6 to 38.7mm. On average, the model demonstrated sub-millimeter precision and>0.95 Dice coefficient compared to the ground truth motion observed in the currently imaged plane. The average Dice coefficient and centroid distance between the model-tracked and ground truth target contours were 0.96±0.03 and 0.26mm±0.27mm respectively across all volunteer studies. The out-of-plane centroid motion error was estimated to be 0.85mm±1.07mm and 1.26mm±1.38mm using the back-propagation (BP) and interpolation error estimation methods. The healthy volunteer studies indicate promising results using the proposed motion modeling technique. Out-of-plane modeling error was estimated to be higher but still demonstrated sub-voxel motion accuracy.

Clinical validation of a prognostic preclinical magnetic resonance imaging biomarker for radiotherapy outcome in head-and-neck cancer.

To retrain a model based on a previously identified prognostic imaging biomarker using apparent diffusion coefficient (ADC) values from diffusion-weighted magnetic resonance imaging (DW-MRI) in a preclinical setting and validate the model using clinical DW-MRI data of patients with locally advanced head-and-neck cancer (HNC) acquired before radiochemotherapy. A total of 31 HNC patients underwent T2-weighted and DW-MRI using 3 T MRI before radiochemotherapy (35x2Gy). Gross tumor volumes (GTV) were delineated based on T2-weighted and b500 images. A preclinical model previously revealed that the size of high-risk subvolumes (HRS) defined by a band of ADC-values was correlated to radiation resistance. To validate this model, different bands of ADC-values were tested using two-sided thresholds on the low-ADC histogram flank to determine HRSs inside the GTV and correlated to treatment outcome after three years. The best model was used to fit a logistic regression model. Stratification potential regarding outcome was internally validated using bootstrap, receiver-operator-characteristic (ROC)-analysis, Kaplan-Meier- and Cox-method, and compared to GTV ADCmean and clinical factors. The best model was defined by 800<ADC<1100∙10-6mm2/s and correlated significantly to treatment outcome (p = 0.003). Optimal HRS cut-off value was found to be 5.8 cm3 according to ROC-analysis. This HRS demonstrated highly significant stratification potential (p < 0.001, bootstrap AUC ≥ 0.84) similar to GTV size (p < 0.001, AUC ≥ 0.79), in contrast to ADCmean (p = 0.361, AUC = 0.53). A preclinical prognostic model defined by an ADC-based HRS was successfully retrained and validated in HNC patients treated with radiochemotherapy. After thorough external validation, such functional HRS based on a band of ADC values may in the future allow interventional response-adaptive MRI-guided radiotherapy in online and offline approaches.

Analysis and Perspectives of MRI-Guided Proton Therapy Integrated Technology

This article explores the MRI-guided proton therapy (MRiPT) integrated technology, which is magnetic resonance imaging-guided radiation therapy. By precisely controlling dose distribution, MRiPT has the potential to enhance the efficacy of radiotherapy. This article reviews the development of MRiPT, analyzes its potential for clinical application, and discusses technical challenges and potential solutions. MRiPT technology offers significant advantages in improving treatment precision and reducing side effects, especially showing exceptional potential in the treatment of complex tumors. This article concludes by looking forward to the future development of MRiPT technology, emphasizes the importance of optimizing electromagnetic compatibility and reducing inter-system interference.

Surrogate gating strategies for the Elekta Unity MR-Linac gating system.

MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM. Surrogate tracking strategies were developed from a cohort of patients treated using the CMM system on the Unity MR-Linac for treatment sites of the lung, pancreas, liver, and prostate. These sites posed challenging visualization or tracking of the primary target thereby compromising the tracking accuracy. Surrogate structures were developed using site-specific strategies to improve the imaging textured detail within the tracking volume while avoiding the dynamic overwhelming hypo- or hyper-intense anatomical structures. These surrogate volumes were applied within the anatomical positioning monitoring system as a proxy that drove the CMM gating decisions on the treatment unit. Robust site-specific surrogate structures were developed. Surrogate tracking structures for centrally located thoracic targets were created by expanding the target peripherally away from the heart and great vessels and into the lung. Pancreas surrogates required a vertically expanded column intersecting with the inferior liver edge. For the liver and prostate, surrogate structures consisted of a uniform expansion of the target, with liver surrogates intersecting the proximal liver edge or diaphragm while avoiding nearby ribs. These surrogate strategies have enabled the gating of complex moving targets among different treatment sites at our institution.

Magnetic resonance imaging-guided single-fraction preoperative radiotherapy for early-stage breast cancer (the RICE trial): feasibility study

BackgroundOver the past decade, the adoption of screening programs, digital mammography, and magnetic resonance imaging (MRI) has increased early-stage breast cancer diagnosis rates. Mortality rates have decreased due to early detection and improved treatments, including personalized therapies. Accelerated partial-breast irradiation (APBI) is emerging as a convenient and effective treatment for some patients, with studies exploring its preoperative use. Preoperative APBI, especially with MRI guidance, offers improved tumor targeting and potentially reduced side effects. Magnetic Resonance Imaging-Guided Single-Fraction Pre-Operative Radiotherapy for Early-Stage Breast Cancer (RICE trial) aims to assess the feasibility and efficacy of MRI-guided single-dose radiotherapy (RT) for early-stage breast cancer.MethodsThe RICE study is a prospective, single-arm study evaluating single-fraction preoperative, APBI treatment for patients with early-stage breast cancer using a magnetic resonance imaging linear accelerator (MRI linac). Eligible patients enrolled in this study will have a core biopsy to confirm estrogen receptor-positive and HER2-negative sub-type. RT planning will use a planning computed tomography (CT) co-registered with a MRI with the patient in either the supine or prone position. For the diagnostic workup, [18F] fluorodeoxyglucose positron emission tomography/CT ([18F] FDG PET/CT) and [18F] fluoroestradiol positron emission tomography/CT ([18F] FES PET/CT) will be performed prior to treatment. Thirty patients will receive a single ablative RT dose of 21 Gray to the tumor. Pre-treatment and post-treatment MRI scans will be acquired at baseline and 5 weeks post-RT respectively. Breast-conserving surgery will be scheduled for 6 weeks after APBI treatment using the MRI linac.The primary study endpoint is the successful administration of a single fraction of preoperative breast RT under the guidance of an MRI linac. Secondary endpoints include evaluating the utility of MRI, [18F] FDG PET/CT, and [18F] FES PET/CT as a non-invasive method for assessing treatment response in patients undergoing single-fraction preoperative APBI.ConclusionThe RICE trial represents a significant step in breast cancer treatment, offering insights that could lead to treatment protocols with minimized RT appointments and enhanced patient outcomes.Trial registrationThis trial is registered with the Australian New Zealand Clinical Trials Registry (ANZCTR). Registered 31st of May 2021. Registration number: ACTRN12621000659808.

A Review of Contemporary Image Guidance Techniques in Head and Neck Cancer.

Traditional head and neck cancer treatment involves open surgery, cytotoxic chemotherapy, and conventional radiotherapy planning. Emerging techniques aim to improve precision and reduce associated toxicity and functional impairment in current practice. This review article describes four such adaptations in image guidance, tailored to next generation therapies. This is a review of current literature, including feasibility studies from our cancer center, relating to: saline-aided intra-oral ultrasound-guided retropharyngeal biopsy; intra-oral ultrasound guided trans-oral robotic surgery (TORS); ultrasound-guided injection of "directly injectedtherapies"; and magnetic resonance imaging-guided radiotherapy. Presented within the context of the wider literature, initial local experience and data indicate good technical outcomes and patient tolerance, and low technical complications in all four image guidance techniques. Initial findings suggest a potentially important future role for these four image guidance techniques, on which next generation therapies are reliant. The broader implications on cross-disciplinary collaboration are also explored herein.

Simulation of Focal Boosting in Online Adaptive MRI-Guided SBRT for Patients With Locally Advanced Prostate Cancer With Seminal Vesicle Involvement

To evaluate the feasibility and accuracy of focal boosting in online adaptive MRI-guided stereotactic body radiation therapy (SBRT) for patients with prostate cancer (PCa) with seminal vesicle invasion (T3b) by analyzing the impact of intrafraction motion on the dose planned for the gross tumor volume (GTV) and clinical target volume (CTV). Data from 23 patients with T1-T3a PCa who received focal boosting SBRT on a 1.5T MR-Linac was used. A radiation oncologist replaced clinical GTVs with artificial GTVs representative for T3b tumor(s). For each MRI used for daily adaptation (MRIadapt), an automated treatment plan was generated (Df1-5) using the adapted contours. Patients were planned to receive 35 Gy to the CTV, with an isotoxic focal boost to the GTV up to 50 Gy. During each fraction, an additional MRI was acquired to assess intrafraction motion (MRIduring). Dose accumulation of all fractions was performed by deformable registration of MRIadapt, f2-5 to MRIadapt, f1 (DACC, planned). The Df1-5 were projected to their corresponding MRIduring, which were used to reconstruct DACC, delivered, likewise. Our results were compared to patients with tumor(s) without seminal vesicle invasion (T1-T3a). The median (10th-90th percentile) D98%ACC, planned to the GTV, which correlates most strongly with outcome, was 41.1 Gy (40.1-43.0 Gy) in the plans for patients with artificial T3b tumors, compared to 43.0 Gy (40.4-47.2 Gy) in the plans for patients with T1-T3a tumors. The D98%ACC, delivered to the GTV, taking into account intrafraction motion, was 41.0 Gy (39.3-42.6 Gy) and 42.5 Gy (40.0-46.6 Gy) in the plans for the artificial and clinical GTVs, respectively. MRI-guidance can ensure high accuracy of focal boosting in patients with T3b disease. Because of the unfavorable location of the GTV, a lower boost dose was feasible compared to patients with T1-T3a PCa.

P188 Stereotactic MRI-guided adaptive radiotherapy for renal tumors in a solitary kidney

P188 Stereotactic MRI-guided adaptive radiotherapy for renal tumors in a solitary kidney

PO0248: MRI-Guided Intensity-Modulated Radiotherapy Boost in Gynecologic Cancers for Patients Unsuitable for Brachytherapy (BT): A Case Series

PO0248: MRI-Guided Intensity-Modulated Radiotherapy Boost in Gynecologic Cancers for Patients Unsuitable for Brachytherapy (BT): A Case Series

Modeling of artificial intelligence-based respiratory motion prediction in MRI-guided radiotherapy: a review.

The advancement of precision radiotherapy techniques, such as volumetric modulated arc therapy (VMAT), stereotactic body radiotherapy (SBRT), and particle therapy, highlights the importance of radiotherapy in the treatment of cancer, while also posing challenges for respiratory motion management in thoracic and abdominal tumors. MRI-guided radiotherapy (MRIgRT) stands out as state-of-art real-time respiratory motion management approach owing to the non-ionizing radiation nature and superior soft-tissue contrast characteristic of MR imaging. In clinical practice, MR imaging often operates at a frequency of 4Hz, resulting in approximately a 300 ms system latency of MRIgRT. This system latency decreases the accuracy of respiratory motion management in MRIgRT. Artificial intelligence (AI)-based respiratory motion prediction has recently emerged as a promising solution to address the system latency issues in MRIgRT, particularly for advanced contour prediction and volumetric prediction. However, implementing AI-based respiratory motion prediction faces several challenges including the collection of training datasets, the selection of prediction methods, and the formulation of complex contour and volumetric prediction problems. This review presents modeling approaches of AI-based respiratory motion prediction in MRIgRT, and provides recommendations for achieving consistent and generalizable results in this field.