Adaptive Radiotherapy Enabled by MRI Guidance

Electron FLASH radiotherapy in vivo studies. A systematic review

FLASH-radiotherapy delivers a radiation beam a thousand times faster compared to conventional radiotherapy, reducing radiation damage in healthy tissues with an equivalent tumor response. Although not completely understood, this radiobiological phenomenon has been proved in several animal models with a spectrum of all kinds of particles currently used in contemporary radiotherapy, especially electrons. However, all the research teams have performed FLASH preclinical studies using industrial linear accelerator or LINAC commonly employed in conventional radiotherapy and modified for the delivery of ultra-high-dose-rate (UHDRs). Unfortunately, the delivering and measuring of UHDR beams have been proved not to be completely reliable with such devices. Concerns arise regarding the accuracy of beam monitoring and dosimetry systems. Additionally, this LINAC totally lacks an integrated and dedicated Treatment Planning System (TPS) able to evaluate the internal dose distribution in the case of in vivo experiments. Finally, these devices cannot modify dose-time parameters of the beam relevant to the flash effect, such as average dose rate; dose per pulse; and instantaneous dose rate. This aspect also precludes the exploration of the quantitative relationship with biological phenomena. The dependence on these parameters need to be further investigated. A promising advancement is represented by a new generation of electron LINAC that has successfully overcome some of these technological challenges. In this review, we aim to provide a comprehensive summary of the existing literature on in vivo experiments using electron FLASH radiotherapy and explore the promising clinical perspectives associated with this technology.

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

BACKGROUND

Magnetic 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.

METHODS

We 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.

RESULTS

Deep 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.

CONCLUSIONS

Deployed 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.

2D antiscatter grid and scatter sampling based CBCT method for online dose calculations during CBCT guided radiation therapy of pelvis

BACKGROUND

Online dose calculations before the delivery of radiation treatments have applications in dose delivery verification, online adaptation of treatment plans, and simulation-free treatment planning. While dose calculations by directly utilizing CBCT images are desired, dosimetric accuracy can be compromised due to relatively lower HU accuracy in CBCT images.

PURPOSE

In this work, we propose a novel CBCT imaging pipeline to enhance the accuracy of CBCT-based dose calculations in the pelvis region. Our approach aims to improve the HU accuracy in CBCT images, thereby improving the overall accuracy of CBCT-based dose calculations prior to radiation treatment delivery.

METHODS

An in-house developed quantitative CBCT pipeline was implemented to address the CBCT raw data contamination problem. The pipeline combines algorithmic data correction strategies and 2D antiscatter grid-based scatter rejection to achieve high CT number accuracy. To evaluate the effect of the quantitative CBCT pipeline on CBCT-based dose calculations, phantoms mimicking pelvis anatomy were scanned using a linac-mounted CBCT system, and a gold standard multidetector CT used for treatment planning (pCT). A total of 20 intensity-modulated treatment plans were generated for five targets, using 6 and 10 MV flattening filter-free beams, and utilizing small and large pelvis phantom images. For each treatment plan, four different dose calculations were performed using pCT images and three CBCT imaging configurations: quantitative CBCT, clinical CBCT protocol, and a high-performance 1D antiscatter grid (1D ASG). Subsequently, dosimetric accuracy was evaluated for both targets and organs at risk as a function of patient size, target location, beam energy, and CBCT imaging configuration.

RESULTS

When compared to the gold-standard pCT, dosimetric errors in quantitative CBCT-based dose calculations were not significant across all phantom sizes, beam energies, and treatment sites. The largest error observed was 0.6% among all dose volume histogram metrics and evaluated dose calculations. In contrast, dosimetric errors reached up to 7% and 97% in clinical CBCT and high-performance ASG CBCT-based treatment plans, respectively. The largest dosimetric errors were observed in bony targets in the large phantom treated with 6 MV beams. The trends of dosimetric errors in organs at risk were similar to those observed in the targets.

CONCLUSIONS

The proposed quantitative CBCT pipeline has the potential to provide comparable dose calculation accuracy to the gold-standard planning CT in photon radiation therapy for the abdomen and pelvis. These robust dose calculations could eliminate the need for density overrides in CBCT images and enable direct utilization of CBCT images for dose delivery monitoring or online treatment plan adaptations before the delivery of radiation treatments.

MV-based relative electron density estimation (iMREDe) for MR-LINAC dose calculation

BACKGROUND

MR-LINAC systems have been increasingly utilized for real-time imaging in adaptive treatments worldwide. Challenges in MR representation of air cavities and subsequent estimation of electron density maps impede planning efficiency and may lead to dose calculation uncertainties.

PURPOSE

To demonstrate the generation of accurate electron density maps using the primary MV beam with a flat-panel imager.

METHODS

The ViewRay MRIdian MR-LINAC system was modeled digitally for Monte Carlo simulations. Iron shimming, the magnetic field, and the proposed flat panel detector were included in the model. The effect of the magnetic field on the detector response was investigated. Acquisition of projections over 360 degrees was simulated for digital phantoms of the Catphan 505 phantom and a patient treated for Head and Neck cancer. Shim patterns on the projections were removed and detector noise linearity was assessed. Electron density maps were generated for the digital patient phantom using the flat-panel detector and compared with actual treatment planning CT generated electron density maps of the same patient.

RESULTS

The effect of the magnetic field on the detector point-spread function (PSF) was found to be substantial for field strengths above 50 mT. Shims correction in the projection images using air normalization and in-painting effectively removed reconstruction artifacts without affecting noise linearity. The relative difference between reconstructed electron density maps from the proposed method and electron density maps generated from the treatment planning CT was 11% on average along all slices included in the iMREDe reconstruction.

CONCLUSIONS

The proposed iMREDe technique demonstrated the feasibility of generating accurate electron densities for the ViewRay MRIdian MR-LINAC system with a flat-panel imager and the primary MV beam. This work is a step towards reducing the time and effort required for adaptive radiotherapy in the current ViewRay MR-LINAC systems.

Real-time motion-including dose estimation of simulated multi-leaf collimator-tracked magnetic resonance-guided radiotherapy

BACKGROUND

Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking.

PURPOSE

We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions.

METHODS

A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25 Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25 Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation.

RESULTS

On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148 ms. Median gamma pass rate (2%/3 mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07 Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12 Gy for the investigated volume and dose criteria respectively.

CONCLUSIONS

Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.

Intensity-Modulated Radiation Therapy for Uterine Cervical Cancer to Reduce Toxicity and Enhance Efficacy - an Option or a Must?: A Narrative Review

Radiotherapy (RT) is a fundamental modality in treatment of cervical cancer. With advancement of technology, conventional RT used for external beam radiotherapy (EBRT) for over half a century has been rapidly replaced with intensity-modulated radiation therapy (IMRT) especially during the last decade. This newer technique is able to differentiate the intensity of radiation within the same field, thus reduces the inevitable exposure of radiation to normal organs and enables better dose delivery to tumors. Recently, the American Society for Radiation Oncology has released a guideline for RT in cervical cancer. Although a section of the guideline recommends IMRT for the purpose of toxicity reduction, a thorough review of the literature is necessary to understand the current status of IMRT in cervical cancer. This narrative review updates the recent high-level evidences regarding the efficacy and toxicity of IMRT and provides a better understanding of the most innovative techniques currently available for EBRT enabled by IMRT.

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

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

Impact of intrafraction motion in pancreatic cancer treatments with MR-guided adaptive radiation therapy

Purpose

The total time of radiation treatment delivery for pancreatic cancer patients with daily online adaptive radiation therapy (ART) on an MR-Linac can range from 50 to 90 min. During this period, the target and normal tissues undergo changes due to respiration and physiologic organ motion. We evaluated the dosimetric impact of the intrafraction physiological organ changes.

Methods

Ten locally advanced pancreatic cancer patients were treated with 50 Gy in five fractions with intensity-modulated respiratory-gated radiation therapy on a 0.35-T MR-Linac. Patients received both pre- and post-treatment volumetric MRIs for each fraction. Gastrointestinal organs at risk (GI-OARs) were delineated on the pre-treatment MRI during the online ART process and retrospectively on the post-treatment MRI. The treated dose distribution for each adaptive plan was assessed on the post-treatment anatomy. Prescribed dose volume histogram metrics for the scheduled plan on the pre-treatment anatomy, the adapted plan on the pre-treatment anatomy, and the adapted plan on post-treatment anatomy were compared to the OAR-defined criteria for adaptation: the volume of the GI-OAR receiving greater than 33 Gy (V33Gy) should be ≤1 cubic centimeter.

Results

Across the 50 adapted plans for the 10 patients studied, 70% were adapted to meet the duodenum constraint, 74% for the stomach, 12% for the colon, and 48% for the small bowel. Owing to intrafraction organ motion, at the time of post-treatment imaging, the adaptive criteria were exceeded for the duodenum in 62% of fractions, the stomach in 36%, the colon in 10%, and the small bowel in 48%. Compared to the scheduled plan, the post-treatment plans showed a decrease in the V33Gy, demonstrating the benefit of plan adaptation for 66% of the fractions for the duodenum, 95% for the stomach, 100% for the colon, and 79% for the small bowel.

Conclusion

Post-treatment images demonstrated that over the course of the adaptive plan generation and delivery, the GI-OARs moved from their isotoxic low-dose region and nearer to the dose-escalated high-dose region, exceeding dose-volume constraints. Intrafraction motion can have a significant dosimetric impact; therefore, measures to mitigate this motion are needed. Despite consistent intrafraction motion, plan adaptation still provides a dosimetric benefit.

Mitigating misalignment in MRI-to-CT synthesis for improved synthetic CT generation: an iterative refinement and knowledge distillation approach

Objective.Deep learning has shown promise in generating synthetic CT (sCT) from magnetic resonance imaging (MRI). However, the misalignment between MRIs and CTs has not been adequately addressed, leading to reduced prediction accuracy and potential harm to patients due to the generative adversarial network (GAN)hallucination phenomenon. This work proposes a novel approach to mitigate misalignment and improve sCT generation.Approach.Our approach has two stages: iterative refinement and knowledge distillation. First, we iteratively refine registration and synthesis by leveraging their complementary nature. In each iteration, we register CT to the sCT from the previous iteration, generating a more aligned deformed CT (dCT). We train a new model on the refined 〈dCT, MRI〉 pairs to enhance synthesis. Second, we distill knowledge by creating a target CT (tCT) that combines sCT and dCT images from the previous iterations. This further improves alignment beyond the individual sCT and dCT images. We train a new model with the 〈tCT, MRI〉 pairs to transfer insights from multiple models into this final knowledgeable model.Main results.Our method outperformed conditional GANs on 48 head and neck cancer patients. It reduced hallucinations and improved accuracy in geometry (3% ↑ Dice), intensity (16.7% ↓ MAE), and dosimetry (1% ↑γ3%3mm). It also achieved <1% relative dose difference for specific dose volume histogram points.Significance.This pioneering approach for addressing misalignment shows promising performance in MRI-to-CT synthesis for MRI-only planning. It could be applied to other modalities like cone beam computed tomography and tasks such as organ contouring.

Gating and intrafraction drift correction on a 1.5 T MR-Linac: Clinical dosimetric benefits for upper abdominal tumors

This work reports on the first seven patients treated with gating and baseline drift correction on the high-field MR-Linac system. Dosimetric analysis showed that the active motion management system improved congruence to the planned dose, efficiently mitigating detrimental effects of intrafraction motion in the upper abdomen.

Experimental validation of multi-fraction online adaptations in magnetic resonance guided radiotherapy

Background and purpose

Radiotherapy plan verification is generally performed on the reference plan based on the pre-treatment anatomy. However, the introduction of online adaptive treatments demands a new approach, as plans are created daily on different anatomies. The aim of this study was to experimentally validate the accuracy of total doses of multi-fraction plan adaptations in magnetic resonance imaging guided radiotherapy in a phantom study, isolated from the uncertainty of deformable image registration.

Materials and methods

We experimentally verified the total dose, measured on external beam therapy 3 (EBT3) film, using a treatment with five online adapted fractions. Three series of experiments were performed, each focusing on a category of inter-fractional variation; translations, rotations and body modifications. Variations were introduced during each fraction and adapted plans were generated and irradiated. Single fraction doses and total doses over five online adapted fractions were investigated.

Results

The online adapted measurements and calculations showed a good agreement for single fractions and multi-fraction treatments for the dose profiles, gamma passing rates, dose deviations and distances to agreement. The gamma passing rate using a 2%/2 mm criterion ranged from 99.2% to 99.5% for a threshold dose of 10% of the maximum dose (Dmax) and from 96.2% to 100% for a threshold dose of 90% of Dmax, for the total translations, rotations and body modifications.

Conclusions

The total doses of multi-fraction treatments showed similar accuracies compared to single fraction treatments, indicating an accurate dosimetric outcome of a multi-fraction treatment in adaptive magnetic resonance imaging guided radiotherapy.

A structured FMEA approach to optimizing combinations of plan-specific quality assurance techniques for IMRT and VMAT QA

BACKGROUND

Many commercial tools are available for plan-specific quality assurance (QA) of radiotherapy plans, with their functionality assessed in isolation. However, multiple QA tools are required to review the full range of potential errors. It is important to assess their effectiveness in combination with each other to look for ways to both streamline the QA process and to make certain that errors of high impact and/or high occurrence are caught before reaching patient treatment.

PURPOSE

To develop a structured method to assess the effective risk reduction of combinations of QA methods for IMRT/VMAT treatments.

METHODS

First, a structured prospective risk assessment was performed to establish the major failure modes (FMs) of IMRT/VMAT QA, and assign occurrence (O), severity (S), and baseline detectability (BD) rankings to them. The baseline assumed that chart checks and linear accelerator QA was performed, but no plan-specific secondary dose calculation or measurement was done. Second, the detectability of each FM for two secondary dose calculation methods and four plan measurement methods (point-based dose calculation, Monte-Carlo-based dose calculation, 2D fluence-based measurement, 2.5D phantom-based measurement, log file analysis with dose recalculation, and log file analysis combined with MLC QA) was determined. Third, we used a minimum detectability approach in addition to each FM's occurrence and severity to determine the optimal combination of QA methods. We analyzed the cumulative risk priority number of eight combinations of QA methods. The analysis was done on (1) all FMs, (2) FMs with high severity, (3) FMs with high-risk priority numbers (RPN) of O*S*BD, and (4) on FMs with both high severity and high RPN.

RESULTS

Our analysis resulted in 54 FMs, including commissioning, planning, data transfer, and linear accelerator failures. 1D secondary dose calculation plus measurement provided a 19%-22% risk reduction from baseline. 1D/3D secondary dose calculation plus log files created a 25%-32% reduction. 3D secondary dose calculation plus measurement resulted in a 27%-34% reduction. 3D secondary dose calculation plus log files with additional machine QA provided the greatest reduction of 31%-42%.

CONCLUSION

This novel structured approach to comparing combinations of QA methods will allow us to optimize our procedures, with the goal of detecting all clinically significant FMs. Our results show that log-file QA with 3D dose recalculation and supplemental machine QA provides better risk reduction than measurement-based QA. This work builds evidence to justify reducing or eliminating measurement-based PSQA with an independent 3D dose verification, log-file measurement, and appropriate supplementation of machine QA. The process also highlights FMs that cannot be caught by pre-treatment QA, prompting us to consider future directions for on-treatment QA.

Accelerating 4D image reconstruction for magnetic resonance-guided radiotherapy

Background and purpose

Physiological motion impacts the dose delivered to tumours and vital organs in external beam radiotherapy and particularly in particle therapy. The excellent soft-tissue demarcation of 4D magnetic resonance imaging (4D-MRI) could inform on intra-fractional motion, but long image reconstruction times hinder its use in online treatment adaptation. Here we employ techniques from high-performance computing to reduce 4D-MRI reconstruction times below two minutes to facilitate their use in MR-guided radiotherapy.

Material and methods

Four patients with pancreatic adenocarcinoma were scanned with a radial stack-of-stars gradient echo sequence on a 1.5T MR-Linac. Fast parallelised open-source implementations of the extra-dimensional golden-angle radial sparse parallel algorithm were developed for central processing unit (CPU) and graphics processing unit (GPU) architectures. We assessed the impact of architecture, oversampling and respiratory binning strategy on 4D-MRI reconstruction time and compared images using the structural similarity (SSIM) index against a MATLAB reference implementation. Scaling and bottlenecks for the different architectures were studied using multi-GPU systems.

Results

All reconstructed 4D-MRI were identical to the reference implementation (SSIM > 0.99). Images reconstructed with overlapping respiratory bins were sharper at the cost of longer reconstruction times. The CPU  + GPU implementation was over 17 times faster than the reference implementation, reconstructing images in 60 ± 1 s and hyper-scaled using multiple GPUs.

Conclusion

Respiratory-resolved 4D-MRI reconstruction times can be reduced using high-performance computing methods for online workflows in MR-guided radiotherapy with potential applications in particle therapy.

The Promise and Future of Radiomics for Personalized Radiotherapy Dosing and Adaptation

Quantitative image analysis, also known as radiomics, aims to analyze large-scale quantitative features extracted from acquired medical images using hand-crafted or machine-engineered feature extraction approaches. Radiomics has great potential for a variety of clinical applications in radiation oncology, an image-rich treatment modality that utilizes computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for treatment planning, dose calculation, and image guidance. A promising application of radiomics is in predicting treatment outcomes after radiotherapy such as local control and treatment-related toxicity using features extracted from pretreatment and on-treatment images. Based on these individualized predictions of treatment outcomes, radiotherapy dose can be sculpted to meet the specific needs and preferences of each patient. Radiomics can aid in tumor characterization for personalized targeting, especially for identifying high-risk regions within a tumor that cannot be easily discerned based on size or intensity alone. Radiomics-based treatment response prediction can aid in developing personalized fractionation and dose adjustments. In order to make radiomics models more applicable across different institutions with varying scanners and patient populations, further efforts are needed to harmonize and standardize the acquisition protocols by minimizing uncertainties within the imaging data.

Predicting the Tumour Response to Radiation by Modelling the Five Rs of Radiotherapy Using PET Images

Despite the intensive use of radiotherapy in clinical practice, its effectiveness depends on several factors. Several studies showed that the tumour response to radiation differs from one patient to another. The non-uniform response of the tumour is mainly caused by multiple interactions between the tumour microenvironment and healthy cells. To understand these interactions, five major biologic concepts called the "5 Rs" have emerged. These concepts include reoxygenation, DNA damage repair, cell cycle redistribution, cellular radiosensitivity and cellular repopulation. In this study, we used a multi-scale model, which included the five Rs of radiotherapy, to predict the effects of radiation on tumour growth. In this model, the oxygen level was varied in both time and space. When radiotherapy was given, the sensitivity of cells depending on their location in the cell cycle was taken in account. This model also considered the repair of cells by giving a different probability of survival after radiation for tumour and normal cells. Here, we developed four fractionation protocol schemes. We used simulated and positron emission tomography (PET) imaging with the hypoxia tracer 18F-flortanidazole (18F-HX4) images as input data of our model. In addition, tumour control probability curves were simulated. The result showed the evolution of tumours and normal cells. The increase in the cell number after radiation was seen in both normal and malignant cells, which proves that repopulation was included in this model. The proposed model predicts the tumour response to radiation and forms the basis for a more patient-specific clinical tool where related biological data will be included.

Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives

Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.

Intrafraction motion analysis in online adaptive radiotherapy for esophageal cancer

Intrafraction motion during magnetic resonance (MR)-guided dose delivery of esophageal cancer tumors was retrospectively analyzed. Deformable image registration of cine-MR series resulted in gross tumor volume motion profiles in all directions, which were subsequently filtered to isolate respiratory and drift motion. A large variability in intrafraction motion patterns was observed between patients. Median 95% peak-to-peak motion was 7.7 (3.7 - 18.3) mm, 2.1 (0.7 - 5.7) mm and 2.4 (0.5 - 5.6) mm in cranio-caudal, left-right and anterior-posterior directions, relatively. Furthermore, intrafraction drift was generally modest (<5mm). A patient specific approach could lead to very small margins (<3mm) for most patients.

Accuracy and Feasibility of Synthetic CT for Lung Adaptive Radiotherapy: A Phantom Study

OBJECTIVES

The respiratory variations will lead to inconsistency between the actual delivery dose and the planning dose. How the minor interfractional amplitude changes affect the geometry and dose delivery accuracy remains to be investigated in the context of lung adaptive radiotherapy.

METHODS

Planning 4-dimensional-computed tomography and kV-cone beam computed tomography were scanned based on the Computerized Imaging Reference Systems phantom, which was employed to simulate the minor interfractional amplitude variations. The corresponding synthetic computed tomography for a particular motion pattern can be generated from Velocity program. Then a clinically meaningful synthetic computed tomography was analyzed through the geometrical and dosimetric assessment.

RESULTS

The image quality of synthetic computed tomography was improved obviously compared with cone beam computed tomography. Mean absolute error was minimized when no significant interfractional motion occurs and Velocity can be qualified for dealing with the regular breathing motion patterns. The mean percent hounsfield unit difference of the synthetic hounsfield unit values per organ relative to the planning 4-dimensional-computed tomography image was 22.3%. Under the same conditions, the mean percent hounsfield unit difference of the cone beam computed tomography hounsfield unit values per organ, relative to the planning 4-dimensional-computed tomography image was 83.9%. Overall, the accuracy of hounsfield unit in synthetic computed tomography was improved obviously and the variability of the synthetic image correlates with the planning 4-dimensional-computed tomography image variability. Meanwhile, the dose-volume histograms between planning 4-dimensional-computed tomography and synthetic computed tomography almost coincided each other, which indicates that Velocity program can qualify lung adaptive radiotherapy well when there were no interfractional respiratory variations. However, for cases with obvious interfractional amplitude change, the volume covered at least by 100% of the prescription dose was only 59.6% for that synthetic image.

CONCLUSION

The synthetic computed tomography images generated from Velocity were close to the real images in anatomy and dosimetry, which can make clinical lung adaptive radiotherapy possible based on the actual patient anatomy during treatment.

Analysis of online plan adaptation for 1.5T magnetic resonance-guided stereotactic body radiotherapy (MRgSBRT) of prostate cancer

PURPOSE

To analyze and characterize the online plan adaptation of 1.5T magnetic resonance-guided stereotactic body radiotherapy (MRgSBRT) of prostate cancer (PC).

METHODS

PC patients (n = 107) who received adaptive 1.5 Tesla MRgSBRT were included. Online plan adaptation was implemented by either the adapt-to-position (ATP) or adapt-to-shape (ATS) methods. Patients were assigned to the ATS group if they underwent ≥ 1 ATS fraction (n = 51); the remainder were assigned to the ATP group (n = 56). The online plan adaptation records of 535 (107 × 5) fractions were retrospectively reviewed. Rationales for ATS decision-making were determined and analyzed using predefined criteria. Statistics of ATS fractions were summarized. Associations of patient characteristics and clinical factors with ATS utilization were investigated.

RESULTS

There were 87 (16.3%) ATS fractions and 448 ATP fractions (83.7%). The numbers of ATS adoptions in fractions 1-5 were 29 (29/107, 27.1%), 18 (16.8%), 15 (14.0%), 16 (15.0%), and 9 (8.4%), respectively, with significant differences in adoption frequency between fractions (p = 0.007). Other baseline patient characteristics and clinical factors were not significantly associated with ATS classification (all p > 0.05). Underlying criteria for the determination of ATS implementation comprised anatomical changes (77 fractions in 50 patients) and discrete multiple targets (15 fractions in 3 patients). No ATS utilization was determined using dosimetric or online quality assurance criteria.

CONCLUSIONS

This study contributes to facilitating the establishment of a standardized protocol for online MR-guided adaptive radiotherapy in PC.

The future of MRI in radiation therapy: Challenges and opportunities for the MR community

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs-at-risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field.

An independent Monte Carlo-based IMRT QA tool for a 0.35 T MRI-guided linear accelerator

PURPOSE

To develop an independent log file-based intensity-modulated radiation therapy (IMRT) quality assurance (QA) tool for the 0.35 T magnetic resonance-linac (MR-linac) and investigate the ability of various IMRT plan complexity metrics to predict the QA results. Complexity metrics related to tissue heterogeneity were also introduced.

METHODS

The tool for particle simulation (TOPAS) Monte Carlo code was utilized with a previously validated linac head model. A cohort of 29 treatment plans was selected for IMRT QA using the developed QA tool and the vendor-supplied adaptive QA (AQA) tool. For 27 independent patient cases, various IMRT plan complexity metrics were calculated to assess the deliverability of these plans. A correlation between the gamma pass rates (GPRs) from the AQA results and calculated IMRT complexity metrics was determined using the Pearson correlation coefficients. Tissue heterogeneity complexity metrics were calculated based on the gradient of the Hounsfield units.

RESULTS

The median and interquartile range for the TOPAS GPRs (3%/3 mm criteria) were 97.24% and 3.75%, respectively, and were 99.54% and 0.36% for the AQA tool, respectively. The computational time for TOPAS ranged from 4 to 8 h to achieve a statistical uncertainty of <1.5%, whereas the AQA tool had an average calculation time of a few minutes. Of the 23 calculated IMRT plan complexity metrics, the AQA GPRs had correlations with 7 out of 23 of the calculated metrics. Strong correlations (|r| > 0.7) were found between the GPRs and the heterogeneity complexity metrics introduced in this work.

CONCLUSIONS

An independent MC and log file-based IMRT QA tool was successfully developed and can be clinically deployed for offline QA. The complexity metrics will supplement QA reports and provide information regarding plan complexity.

To see or not to see: Evaluation of magnetic resonance imaging sequences for use in MR Linac-based radiotherapy treatment

BACKGROUND/PURPOSE

This work evaluated the suitability of MR derived sequences for use in online adaptive RT workflows on a 1.5 Tesla (T) MR-Linear Accelerator (MR Linac).

MATERIALS/METHODS

Non-patient volunteers were recruited to an ethics approved MR Linac imaging study. Participants attended 1-3 imaging sessions in which a combination of DIXON, 2D and 3D volumetric T1 and T2 weighted images were acquired axially, with volunteers positioned using immobilisation devices typical for radiotherapy to the anatomical region being scanned. Images from each session were appraised by three independent reviewers to determine optimal sequences over six anatomical regions: head and neck, female and male pelvis, thorax (lung), thorax (breast/chest wall) and abdomen. Site specific anatomical structures were graded by the perceived ability to accurately contour a typical organ at risk. Each structure was independently graded on a 4-point Likert scale as 'Very Clear', 'Clear', 'Unclear' or 'Not visible' by observers, consisting of radiographers (therapeutic and diagnostic) and clinicians.

RESULTS

From July 2019 to September 2019, 18 non-patient volunteers underwent 24 imaging sessions in the following anatomical regions: head and neck (n=3), male pelvis (n=4), female pelvis (n=5), lung/oesophagus (n=5) abdomen (n=4) and chest wall/breast (n=3). T2 sequences were the most preferred for perceived ability to contour anatomy in both male and female pelvis. For all other sites T1 weighted DIXON sequences were most favourable.

CONCLUSION

This study has determined the preferential sequence selection for organ visualisation, as a pre-requisite to our institution adopting MR-guided radiotherapy for a more diverse range of disease sites.

Feasibility of tumour-focused adaptive radiotherapy for bladder cancer on the MR-linac

Bladder tumour-focused magnetic resonance image-guided adaptive radiotherapy using a 1.5 Tesla MR-linac is feasible. A full online workflow adapting to anatomy at each fraction is achievable in approximately 30 min. Intra-fraction bladder filling did not compromise target coverage with the class solution employed.

Effect of intrafraction adaptation on PTV margins for MRI guided online adaptive radiotherapy for rectal cancer

Purpose

To determine PTV margins for intrafraction motion in MRI-guided online adaptive radiotherapy for rectal cancer and the potential benefit of performing a 2nd adaptation prior to irradiation.

Methods

Thirty patients with rectal cancer received radiotherapy on a 1.5 T MR-Linac. On T2-weighted images for adaptation (MRI_adapt), verification prior to (MRI_ver) and after irradiation (MRI_post) of 5 treatment fractions per patient, the primary tumor GTV (GTV_prim) and mesorectum CTV (CTV_meso) were delineated. The structures on MRI_adapt were expanded to corresponding PTVs. We determined the required expansion margins such that on average over 5 fractions, 98% of CTV_meso and 95% of GTV_prim on MRI_post was covered in 90% of the patients. Furthermore, we studied the benefit of an additional adaptation, just prior to irradiation, by evaluating the coverage between the structures on MRI_ver and MRI_post. A threshold to assess the need for a secondary adaptation was determined by considering the overlap between MRI_adapt and MRI_ver.

Results

PTV margins for intrafraction motion without 2nd adaptation were 6.4 mm in the anterior direction and 4.0 mm in all other directions for CTV_meso and 5.0 mm isotropically for GTV_prim. A 2nd adaptation, applied for all fractions where the motion between MRI_adapt and MRI_ver exceeded 1 mm (36% of the fractions) would result in a reduction of the PTV_meso margin to 3.2 mm/2.0 mm. For PTV_prim a margin reduction to 3.5 mm is feasible when a 2nd adaptation is performed in fractions where the motion exceeded 4 mm (17% of the fractions).

Conclusion

We studied the potential benefit of intrafraction motion monitoring and a 2nd adaptation to reduce PTV margins in online adaptive MRIgRT in rectal cancer. Performing 2nd adaptations immediately after online replanning when motion exceeded 1 mm and 4 mm for CTV_meso and GTV_prim respectively, could result in a 30–50% margin reduction with limited reduction of dose to the bowel.

Deep Learning for Per-Fraction Automatic Segmentation of Gross Tumor Volume (GTV) and Organs at Risk (OARs) in Adaptive Radiotherapy of Cervical Cancer

Background/Hypothesis

MRI-guided online adaptive radiotherapy (MRI-g-OART) improves target coverage and organs-at-risk (OARs) sparing in radiation therapy (RT). For patients with locally advanced cervical cancer (LACC) undergoing RT, changes in bladder and rectal filling contribute to large inter-fraction target volume motion. We hypothesized that deep learning (DL) convolutional neural networks (CNN) can be trained to accurately segment gross tumor volume (GTV) and OARs both in planning and daily fractions' MRI scans.

Materials/Methods

We utilized planning and daily treatment fraction setup (RT-Fr) MRIs from LACC patients, treated with stereotactic body RT to a dose of 45-54 Gy in 25 fractions. Nine structures were manually contoured. MASK R-CNN network was trained and tested under three scenarios: (i) Leave-one-out (LOO), using the planning images of N- 1 patients for training; (ii) the same network, tested on the RT-Fr MRIs of the "left-out" patient, (iii) including the planning MRI of the "left-out" patient as an additional training sample, and tested on RT-Fr MRIs. The network performance was evaluated using the Dice Similarity Coefficient (DSC) and Hausdorff distances. The association between the structures' volume and corresponding DSCs was investigated using Pearson's Correlation Coefficient, r.

Results

MRIs from fifteen LACC patients were analyzed. In the LOO scenario the DSC for Rectum, Femur, and Bladder was >0.8, followed by the GTV, Uterus, Mesorectum and Parametrium (0.6-0.7). The results for Vagina and Sigmoid were suboptimal. The performance of the network was similar for most organs when tested on RT-Fr MRI. Including the planning MRI in the training did not improve the segmentation of the RT-Fr MRI. There was a significant correlation between the average organ volume and the corresponding DSC (r = 0.759, p = 0.018).

Conclusion

We have established a robust workflow for training MASK R-CNN to automatically segment GTV and OARs in MRI-g-OART of LACC. Albeit the small number of patients in this pilot project, the network was trained to successfully identify several structures while challenges remain, especially in relatively small organs. With the increase of the LACC cases, the performance of the network will improve. A robust auto-contouring tool would improve workflow efficiency and patient tolerance of the OART process.

Acquisition repeatability of MRI radiomics features in the head and neck: a dual-3D-sequence multi-scan study

Radiomics has increasingly been investigated as a potential biomarker in quantitative imaging to facilitate personalized diagnosis and treatment of head and neck cancer (HNC), a group of malignancies associated with high heterogeneity. However, the feature reliability of radiomics is a major obstacle to its broad validity and generality in application to the highly heterogeneous head and neck (HN) tissues. In particular, feature repeatability of radiomics in magnetic resonance imaging (MRI) acquisition, which is considered a crucial confounding factor of radiomics feature reliability, is still sparsely investigated. This study prospectively investigated the acquisition repeatability of 93 MRI radiomics features in ten HN tissues of 15 healthy volunteers, aiming for potential magnetic resonance-guided radiotherapy (MRgRT) treatment of HNC. Each subject underwent four MRI acquisitions with MRgRT treatment position and immobilization using two pulse sequences of 3D T1-weighed turbo spin-echo and 3D T2-weighed turbo spin-echo on a 1.5 T MRI simulator. The repeatability of radiomics feature acquisition was evaluated in terms of the intraclass correlation coefficient (ICC), whereas within-subject acquisition variability was evaluated in terms of the coefficient of variation (CV). The results showed that MRI radiomics features exhibited heterogeneous acquisition variability and uncertainty dependent on feature types, tissues, and pulse sequences. Only a small fraction of features showed excellent acquisition repeatability (ICC > 0.9) and low within-subject variability. Multiple MRI scans improved the accuracy and confidence of the identification of reliable features concerning MRI acquisition compared to simple test-retest repeated scans. This study contributes to the literature on the reliability of radiomics features with respect to MRI acquisition and the selection of reliable radiomics features for use in modeling in future HNC MRgRT applications.

Dosimetric benefit of MR-guided online adaptive radiotherapy in different tumor entities: liver, lung, abdominal lymph nodes, pancreas and prostate

Background

Hybrid magnetic resonance (MR)-Linac systems have recently been introduced into clinical practice. The systems allow online adaption of the treatment plan with the aim of compensating for interfractional anatomical changes. The aim of this study was to evaluate the dose volume histogram (DVH)-based dosimetric benefits of online adaptive MR-guided radiotherapy (oMRgRT) across different tumor entities and to investigate which subgroup of plans improved the most from adaption.

Methods

Fifty patients treated with oMRgRT for five different tumor entities (liver, lung, multiple abdominal lymph nodes, pancreas, and prostate) were included in this retrospective analysis. Various target volume (gross tumor volume GTV, clinical target volume CTV, and planning target volume PTV) and organs at risk (OAR) related DVH parameters were compared between the dose distributions before and after plan adaption.

Results

All subgroups clearly benefited from online plan adaption in terms of improved PTV coverage. For the liver, lung and abdominal lymph nodes cases, a consistent improvement in GTV coverage was found, while many fractions of the prostate subgroup showed acceptable CTV coverage even before plan adaption. The largest median improvements in GTV near-minimum dose (D_98%) were found for the liver (6.3%, p < 0.001), lung (3.9%, p < 0.001), and abdominal lymph nodes (6.8%, p < 0.001) subgroups. Regarding OAR sparing, the largest median OAR dose reduction during plan adaption was found for the pancreas subgroup (-87.0%). However, in the pancreas subgroup an optimal GTV coverage was not always achieved because sparing of OARs was prioritized.

Conclusion

With online plan adaptation, it was possible to achieve significant improvements in target volume coverage and OAR sparing for various tumor entities and account for interfractional anatomical changes.

A multi-institutional comparison of dosimetric data for a 0.35 T MR-linac

Objective. A comparison of percent depth dose (PDD) curves, lateral beam profiles, output factors (OFs), multileaf collimator (MLC) leakage, and couch transmission factors was performed between ten institutes for a commercial 0.35 T MR-linac. Approach. The measured data was collected during acceptance testing of the MR-linac. The PDD curves were measured for the 3.32 × 3.32 cm2, 9.96 × 9.96 cm2, and 27.20 × 24.07 cm2 field sizes. The lateral beam profiles were acquired for a 27.20 × 24.07 cm2 field size using an ion chamber array and penumbra was defined as the distance between 80% of the maximum dose and 20% of the maximum dose after normalizing the profiles to the dose at the inflection points. The OFs were measured using solid-state dosimeters, whereas radiochromic films were utilized to measure radiation leakage through the MLC stacks. The relative couch transmission factors were measured for various gantry angles. The variation in the multi-institutional data was quantified using the percent standard deviation metric. Main results. Minimal variations (<1%) were found between the PDD data, except for the build-up region and the deeper regions of the PDD curve. The in-field region of the lateral beam profiles varied <1.5% between different institutions and a small variation (<0.7 mm) in penumbra was observed. A variation of <1% was observed in the OF data for field sizes above 1.66 × 1.66 cm2, whereas large variations were shown for small-field sizes. The average and maximum MLC leakage was calculated to be <0.3% and <0.6%, which was well below the international electrotechnical commission (IEC) leakage thresholds. The couch transmission was smallest for oblique beams and ranged from 0.83 to 0.87. Significance. The variation in the data was found to be relatively small and the different 0.35 T MR-linacs were concluded to have similar dosimetric characteristics.

Quality management in radiotherapy treatment delivery

Radiation Oncology continues to rely on accurate delivery of radiation, in particular where patients can benefit from more modulated and hypofractioned treatments that can deliver higher dose to the target while optimising dose to normal structures. These deliveries are more complex, and the treatment units are more computerised, leading to a re-evaluation of quality assurance (QA) to test a larger range of options with more stringent criteria without becoming too time and resource consuming. This review explores how modern approaches of risk management and automation can be used to develop and maintain an effective and efficient QA programme. It considers various tools to control and guide radiation delivery including image guidance and motion management. Links with typical maintenance and repair activities are discussed, as well as patient-specific quality control activities. It is demonstrated that a quality management programme applied to treatment delivery can have an impact on individual patients but also on the quality of treatment techniques and future planning. Developing and customising a QA programme for treatment delivery is an important part of radiotherapy. Using modern multidisciplinary approaches can make this also a useful tool for department management.

Clinical implementation and feasibility of long-course fractionated MR-guided chemoradiotherapy for patients with esophageal cancer: an R-IDEAL stage 1b/2a evaluation of technical innovation

•

Online MR-guided long-course fractionated chemoradiotherapy for patients with esophageal cancer was feasible in 7 out of 9 patients.

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Median treatment time was 53 min per fraction.

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MRgRT resulted in a reduction in mean heart dose (12%) and mean lung dose (26%) compared to CBCT-guided radiotherapy.

•

Limited intrafraction motion was observed during dose delivery.

Purpose

This R-Ideal stage 1b/2a study describes the workflow and feasibility of long-course fractionated online adaptive MR-guided chemoradiotherapy with reduced CTV-to-PTV margins on the 1.5T MR-Linac for patients with esophageal cancer.

Methods

Patients with esophageal cancer scheduled to undergo chemoradiation were treated on a 1.5T MR-Linac. Daily MR-images were acquired for online contour adaptation and replanning. Contours were manually adapted to match the daily anatomy and an isotropic CTV-to-PTV margin of 6 mm was applied. Time was recorded for all individual steps in the workflow. Feasibility and patient tolerability were defined as on-table time of ≤60 min and completion of >95% of the fractions on the MR-Linac, respectively. Positioning verification and post-treatment MRIs were retrospectively analyzed and dosimetric parameters were compared to standard non-adaptive conventional treatment plans.

Results

Nine patients with esophageal cancer were treated with chemoradiation; eight patients received 41.4 Gy in 23 fractions and one received 50.4 Gy in 28 fractions. Four patients received all planned fractions on the MR-Linac, whereas for two patients >5% of fractions were rescheduled to a conventional linac for reasons of discomfort. A total of 183 (86%) of 212 scheduled fractions were successfully delivered on the MR-Linac. Three fractions ended prematurely due to technical issues and 26 fractions were rescheduled on a conventional linac due to MR-Linac downtime (n = 10), logistical reasons (n = 3) or discomfort (n = 13).

The median time per fraction was 53 min (IQR = 3 min). Daily adapted MR-Linac plans had similar target coverage, whereas dose to the organs-at-risk was significantly reduced compared to conventional treatment (26% and 12% reduction in mean lung and heart dose, respectively).

Conclusion

Daily online adaptive fractionated chemoradiotherapy with reduced PTV margins is moderately feasible for esophageal cancer and results in better sparing of heart and lungs. Future studies should focus on further optimization and acceleration of the current workflow.

Radiation therapy for prostate cancer: What's the best in 2021

Radiotherapy is highly involved in the management of prostate cancer. Its features and potential applications experienced a radical evolution over last decades, as they are associated to the continuous evolution of available technology and current oncological innovations. Some application of radiotherapy like brachytherapy have been recently enriched by innovative features and multidisciplinary dedications. In this report we aim to put some questions regarding the following issues regarding multiple aspects of modern application of radiation oncology: the current application of radiation oncology; the modern role of stereotactic body radiotherapy (SBRT) for both the management of primary lesions and for lymph-nodal recurrence; the management of the oligometastatic presentations; the role of brachytherapy; the aid played by the application of the organ at risk spacer (spacer OAR), fiducial markers, electromagnetic tracking systems and on-line Magnetic Resonance guided radiotherapy (MRgRT), and the role of the new opportunity represented by radiomic analysis.

Toward MR-integrated proton therapy: modeling the potential benefits for liver tumors

Magnetic resonance imaging (MRI)-integrated proton therapy (MRiPT) is envisioned to improve treatment quality for many cancer patients. However, given the availability of alternative image-guided strategies, its clinical need is yet to be justified. This study aims to compare the expected clinical outcomes of MRiPT with standard of practice cone-beam CT (CBCT)-guided PT, and other MR-guided methods, i.e. offline MR-guided PT and MR-linac, for treatment of liver tumors. Clinical outcomes were assessed by quantifying the dosimetric and biological impact of target margin reduction enabled by each image-guided approach. Planning target volume (PTV) margins were calculated using random and systematic setup, delineation and motion uncertainties, which were quantified by analyzing longitudinal MRI data for 10 patients with liver tumors. Proton treatment plans were created using appropriate PTV margins for each image-guided PT method. Photon plans with margins equivalent to MRiPT were generated to represent MR-linac. Normal tissue complication probabilities (NTCP) of the uninvolved liver were compared. We found that PTV margin can be reduced by 20% and 40% for offline MR-guided PT and MRiPT, respectively, compared with CBCT-guided PT. Furthermore, clinical target volume expansion could be largely alleviated when delineating on MRI rather than CT. Dosimetric implications included decreased equivalent mean dose of the uninvolved liver, i.e. up to 24.4 Gy and 27.3 Gy for offline MR-guided PT and MRiPT compared to CBCT-guided PT, respectively. Considering Child-Pugh score increase as endpoint, NTCP of the uninvolved liver was significantly decreased for MRiPT compared to CBCT-guided PT (up to 48.4%,p < 0.01), offline MR-guided PT (up to 12.9%,p < 0.01) and MR-linac (up to 30.8%,p < 0.05). Target underdose was possible in the absence of MRI-guidance (D90 reduction up to 4.2 Gy in 20% of cases). In conclusion, MRiPT has the potential to significantly reduce healthy liver toxicities in patients with liver tumors. It is superior to other image-guided techniques currently available.

Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment

Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.

Clinical outcomes of stereotactic magnetic resonance image-guided adaptive radiotherapy for primary and metastatic tumors in the abdomen and pelvis

Purpose

Stereotactic body radiotherapy (SBRT) delivers ablative doses with excellent local control. However, implementing SBRT for abdominal and pelvic tumors has been limited by the risk for treatment‐related gastrointestinal toxicity. MRI‐guided radiotherapy may ameliorate these risks and increase the therapeutic ratio. We report the clinical outcomes of stereotactic MRI‐guided adaptive radiotherapy (SMART) for primary and metastatic tumors in the abdomen and pelvis.

Methods

From November 2014 to August 2017, the first 106 consecutive patients with 121 tumors in the abdomen and pelvis were treated with SMART at a single institution. Of the cohort, 41.5%, 15.1%, and 43.4% had primary, locally recurrent, and oligometastatic tumors, respectively. SMART was delivered using a tri‐cobalt‐60 gantry with on‐board 0.35 Tesla MRI with respiratory breath‐hold and daily adaptive re‐planning when anatomically necessary. A median of 40Gy in five fractions was prescribed. The Common Terminology Criteria for Adverse Events v.4.03 was used to score treatment‐related toxicities. Local control (LC), progression‐free survival (PFS), and overall survival (OS) were estimated using Kaplan–Meier method.

Results

Of the 510 treatments, seventy‐one (13.9%) were adapted. Fatigue, nausea, and pain were the most common acute toxicities. 0.9 and 0% of patients experienced acute grade three and four toxicities, respectively. 5.2 and 2.1% of patients experienced late grade three and four toxicities, respectively. After a median follow‐up of 20.4 months, the 2‐year LC rate was 74% on a per‐lesion basis. Two‐year LC was 96% for lesions that were treated with BED₁₀≥100 versus 69% for BED₁₀<100 (p = 0.02). PFS was significantly different between patients with and without locally controlled tumors (2‐year PFS 21 vs. 8%, p = 0.03). Two‐year OS was 57% for the entire cohort.

Conclusions

Favorable LC and PFS outcomes were observed with minimal morbidity for tumors in the abdomen and pelvis treated with SMART. Future prospective clinical trials to validate these findings are warranted.

Adaptive Radiotherapy in the Management of Cervical Cancer: Review of Strategies and Clinical Implementation

The complex and varied motion of the cervix-uterus target during external beam radiotherapy (EBRT) underscores the clinical benefits afforded by adaptive radiotherapy (ART) techniques. These gains have already been realised in the implementation of image-guided adaptive brachytherapy, where adapting to anatomy at each fraction has seen improvements in clinical outcomes and a reduction in treatment toxicity. With regards to EBRT, multiple adaptive strategies have been implemented, including a personalised internal target volume, offline replanning and a plan of the day approach. With technological advances, there is now the ability for real-time online ART using both magnetic resonance imaging and computed tomography-guided imaging. However, multiple challenges remain in the widespread dissemination of ART. This review investigates the ART strategies and their clinical implementation in EBRT delivery for cervical cancer.

Is Ultra Hypofractionated Radiation Therapy a Safe and Effective Treatment for Invasive Bladder Cancer in the Elderly?: A Retrospective Single Institution Review

PURPOSE

The aim was to determine the efficacy, safety, and tolerability of weekly ultra hypofractionated radiation therapy for older unfit patients with invasive bladder cancer.

METHODS

We retrospectively analyzed a cohort of patients with muscle invasive bladder cancer deemed unfit for chemoradiation therapy and thus treated with 6 weekly doses of 6 Gy using intensity modulated radiotherapy. Charlson comorbidity was calculated retrospectively. Cystoscopy and computed tomography were used to evaluate local control and toxicity using the common terminology criteria. Survival outcomes were estimated using the Kaplan-Meier method.

RESULTS

Twenty-two patients with a median age of 84 (range: 70 to 96) years were included. The median comorbidity index was 6±1.5 SD. Nineteen (90%) patients received the full 36 Gy dose. Median follow-up was 10±7 months (range: 6 to 27 mo). Local control in the bladder was achieved in 16 of 19 evaluable patients (84%). One-year overall survival was 62.5%, 1 patient had a retroperitoneal nodal recurrence and 3 patients developed distant metastasis. Grade 3 genitourinary and gastrointestinal toxicity was observed in 4 (18%) and 1 (4.5%) patients, respectively.

CONCLUSION

Weekly ultra hypofractionated intensity modulated radiotherapy with image guidance and bladder training is an effective, safe, and well-tolerated regimen for older patients with invasive bladder cancer unfit for radical treatment.

Recommendations for planning and delivery of radical radiotherapy for localized urothelial carcinoma of the bladder

PURPOSE

Curative radio-chemotherapy is recognized as a standard treatment option for muscle-invasive bladder cancer (MIBC). Nevertheless, the technical aspects for MIBC radiotherapy are heterogeneous with a lack of practical recommendations.

METHODS AND MATERIALS

In 2018, a workshop identified the need for two cooperative groups to develop consistent, evidence-based guidelines for irradiation technique in the delivery of curative radiotherapy. Two radiation oncologists performed a review of the literature addressing several topics relative to radical bladder radiotherapy: planning computed tomography acquisition, target volume delineation, radiation schedules (total dose and fractionation) and dose delivery (including radiotherapy techniques, image-guided radiotherapy (IGRT) and adaptive treatment modalities). Searches for original and review articles in the PubMed and Google Scholar databases were conducted from January 1990 until March 2020. During a meeting conducted in October 2020, results on 32 topics were presented and discussed with a working group involving 15 radiation oncologists, 3 urologists and one medical oncologist. We applied the American Urological Association guideline development's method to define a consensus strategy.

RESULTS

A consensus was obtained for all 34 except 4 items. The group did not obtain an agreement on CT enhancement added value for planning, PTV margins definition for empty bladder and full bladder protocols, and for pelvic lymph-nodes irradiation. High quality evidence was shown in 6 items; 8 items were considered as low quality of evidence.

CONCLUSION

The current recommendations propose a homogenized modality of treatment both for routine clinical practice and for future clinical trials, following the best evidence to date, analyzed with a robust methodology. The XXX group formulates practical guidelines for the implementation of innovative techniques such as adaptive radiotherapy.

Image-guided Adaptive Radiotherapy for Bladder Cancer

Technological advancement has facilitated patient-specific radiotherapy in bladder cancer. This has been made possible by developments in image-guided radiotherapy (IGRT). Particularly transformative has been the integration of volumetric imaging into the workflow. The ability to visualise the bladder target using cone beam computed tomography and magnetic resonance imaging initially assisted with determining the magnitude of inter- and intra-fraction target change. It has led to greater confidence in ascertaining true anatomy at each fraction. The increased certainty of dose delivered to the bladder has permitted the safe reduction of planning target volume margins. IGRT has therefore improved target coverage with a reduction in integral dose to the surrounding tissue. Use of IGRT to feed back into plan and dose delivery optimisation according to the anatomy of the day has enabled adaptive radiotherapy bladder solutions. Here we undertake a review of the stepwise developments underpinning IGRT and adaptive radiotherapy strategies for external beam bladder cancer radiotherapy. We present the evidence in accordance with the framework for systematic clinical evaluation of technical innovations in radiation oncology (R-IDEAL).

Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow

Automated segmentation templates can save clinicians time compared to de novo segmentation but may still take substantial time to review and correct. It has not been thoroughly investigated which automated segmentation-corrected segmentation similarity metrics best predict clinician correction time. Bilateral thoracic cavity volumes in 329 CT scans were segmented by a UNet-inspired deep learning segmentation tool and subsequently corrected by a fourth-year medical student. Eight spatial similarity metrics were calculated between the automated and corrected segmentations and associated with correction times using Spearman’s rank correlation coefficients. Nine clinical variables were also associated with metrics and correction times using Spearman’s rank correlation coefficients or Mann–Whitney U tests. The added path length, false negative path length, and surface Dice similarity coefficient correlated better with correction time than traditional metrics, including the popular volumetric Dice similarity coefficient (respectively ρ = 0.69, ρ = 0.65, ρ =  − 0.48 versus ρ =  − 0.25; correlation p values < 0.001). Clinical variables poorly represented in the autosegmentation tool’s training data were often associated with decreased accuracy but not necessarily with prolonged correction time. Metrics used to develop and evaluate autosegmentation tools should correlate with clinical time saved. To our knowledge, this is only the second investigation of which metrics correlate with time saved. Validation of our findings is indicated in other anatomic sites and clinical workflows. Novel spatial similarity metrics may be preferable to traditional metrics for developing and evaluating autosegmentation tools that are intended to save clinicians time.

Quantitative assessment of acquisition imaging parameters on MRI radiomics features: a prospective anthropomorphic phantom study using a 3D-T2W-TSE sequence for MR-guided-radiotherapy

Background

MRI pulse sequences and imaging parameters substantially influence the variation of MRI radiomics features, thus impose a critical challenge on MRI radiomics reproducibility and reliability. This study aims to prospectively investigate the impact of various imaging parameters on MRI radiomics features in a 3D T2-weighted (T2W) turbo-spin-echo (TSE) pulse sequence for MR-guided-radiotherapy (MRgRT).

Methods

An anthropomorphic phantom was scanned using a 3D-T2W-TSE MRgRT sequence at 1.5T under a variety of acquisition imaging parameter changes. T1 and T2 relaxation times of the phantom were also measured. 93 first-order and texture radiomics features in the original and 14 transformed images, yielding 1,395 features in total, were extracted from 10 volumes-of-interest (VOIs). The percentage deviation (d%) of radiomics feature values from the baseline values and intra-class correlation coefficient (ICC) with the baseline were calculated. Robust radiomics features were identified based on the excellent agreement of radiomics feature values with the baseline, i.e., the averaged d% <5% and ICC >0.90 in all VOIs for all imaging parameter variations.

Results

The radiomics feature values changed considerably but to different degrees with different imaging parameter adjustments, in the ten VOIs. The deviation d% ranged from 0.02% to 321.3%, with a mean of 12.5% averaged for all original features in all ten VOIs. First-order and GLCM features were generally more robust to imaging parameters than other features in the original images. There were also significantly different radiomics feature values (ANOVA, P<0.001) between the original and the transformed images, exhibiting quite different robustness to imaging parameters. 330 out of 1395 features (23.7%) robust to imaging parameters were identified. GLCM and GLSZM features had the most (42.5%, 153/360) and least (3.8%, 9/240) robust features in the original and transformed images, respectively.

Conclusions

This study helps better understand the quantitative dependence of radiomics feature values on imaging parameters in a 3D-T2W-TSE sequence for MRgRT. Imaging parameter heterogeneity should be considered as a significant source of radiomics variability and uncertainty, which must be well harmonized for reliable clinical use. The identified robust features to imaging parameters are helpful for the pre-selection of radiomics features for reliable radiomics modeling.

A phantom study to contrast and compare polymer and gold fiducial markers in radiotherapy simulation imaging

To assess visibility and artifact characteristics of polymer fiducials compared to standard gold fiducials for radiotherapy CT and MRI simulation. Three gold and three polymer fiducials were inserted into a CT and MRI tissue-equivalent phantom that approximated the prostate cancer radiotherapy configuration. The phantom and fiducials were imaged on CT and MRI. Images were assessed in terms of fiducial visibility and artifact. ImageJ was employed to quantify the pixel gray-scale of each fiducial and artifact. Fiducial gray-scale histograms and profiles were generated for analysis. Objective measurements of the contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and artifact index (AI) were calculated. The CT images showed that the gold fiducials are visually brighter, with greater contrast than the polymer. The higher peak values illustrate this in the line profiles. However, they produce bright radiating and dark shadowing artifacts. This is depicted by the greater width of line profiles and the disruption of phantom area profiles. Quantitatively this results in greater percentile ranges of the histograms. Furthermore, for CT, gold had a higher CNR than polymer, relative to the phantom. However, the gold CNR and SNR were degraded by the greater artifact and thus AI. Both fiducials were visible on MRI and had similar histograms and profiles that were also reflected in comparable CNR, SNR and AI. Polymer fiducials were well visualized in a phantom on CT and MR and produce less artifact than the gold fiducials. Polymer markers could enhance the quality and accuracy of radiotherapy co-registration and planning but require clinical confirmation.

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.

Strategies to Minimize Late Effects From Pelvic Radiotherapy

During the past 30 years, radiation treatment techniques have significantly improved, from conventional external-beam radiation therapy, to three-dimensional conformal radiation therapy, to current intensity-modulated radiation therapy, benefiting patients who undergo treatment of pelvic malignancies. Modern treatment options also include proton beam irradiation as well as low and high dose rate brachytherapy. Although the acute adverse effects of these modalities are well documented in clinical trials, less well known are the true incidence and optimal management of those late adverse effects that can occur months to years later. In a population of survivors of cancer that is steadily increasing, with many such patients receiving radiotherapy at some time during their disease course, these late effects can become a considerable management and quality-of-life issue. This review will examine the range of late toxicities that can occur from pelvic radiotherapy and explore strategies to prevent and mitigate them.

MR-Guided Adaptive Radiotherapy for Bladder Cancer

Radiotherapy has an important role in the curative and palliative treatment settings for bladder cancer. As a target for radiotherapy the bladder presents a number of technical challenges. These include poor tumor visualization and the variability in bladder size and position both between and during treatment delivery. Evidence favors the use of magnetic resonance imaging (MRI) as an important means of tumor visualization and local staging. The availability of hybrid systems incorporating both MRI scanning capabilities with the linear accelerator (MR-Linac) offers opportunity for in-room and real-time MRI scanning with ability of plan adaption at each fraction while the patient is on the treatment couch. This has a number of potential advantages for bladder cancer patients. In this article, we examine the technical challenges of bladder radiotherapy and explore how magnetic resonance (MR) guided radiotherapy (MRgRT) could be leveraged with the aim of improving bladder cancer patient outcomes. However, before routine clinical implementation robust evidence base to establish whether MRgRT translates into improved patient outcomes should be ascertained.