MRI-LINAC: A transformative technology in radiation oncology

4D-Precise: Learning-based 3D motion estimation and high temporal resolution 4DCT reconstruction from treatment 2D+t X-ray projections

BACKGROUND AND OBJECTIVE

In radiotherapy treatment planning, respiration-induced motion introduces uncertainty that, if not appropriately considered, could result in dose delivery problems. 4D cone-beam computed tomography (4D-CBCT) has been developed to provide imaging guidance by reconstructing a pseudo-motion sequence of CBCT volumes through binning projection data into breathing phases. However, it suffers from artefacts and erroneously characterizes the averaged breathing motion. Furthermore, conventional 4D-CBCT can only be generated post-hoc using the full sequence of kV projections after the treatment is complete, limiting its utility. Hence, our purpose is to develop a deep-learning motion model for estimating 3D+t CT images from treatment kV projection series.

METHODS

We propose an end-to-end learning-based 3D motion modelling and 4DCT reconstruction model named 4D-Precise, abbreviated from Probabilistic reconstruction of image sequences from CBCT kV projections. The model estimates voxel-wise motion fields and simultaneously reconstructs a 3DCT volume at any arbitrary time point of the input projections by transforming a reference CT volume. Developing a Torch-DRR module, it enables end-to-end training by computing Digitally Reconstructed Radiographs (DRRs) in PyTorch. During training, DRRs with matching projection angles to the input kVs are automatically extracted from reconstructed volumes and their structural dissimilarity to inputs is penalised. We introduced a novel loss function to regulate spatio-temporal motion field variations across the CT scan, leveraging planning 4DCT for prior motion distribution estimation.

RESULTS

The model is trained patient-specifically using three kV scan series, each including over 1200 angular/temporal projections, and tested on three other scan series. Imaging data from five patients are analysed here. Also, the model is validated on a simulated paired 4DCT-DRR dataset created using the Surrogate Parametrised Respiratory Motion Modelling (SuPReMo). The results demonstrate that the reconstructed volumes by 4D-Precise closely resemble the ground-truth volumes in terms of Dice, volume similarity, mean contour distance, and Hausdorff distance, whereas 4D-Precise achieves smoother deformations and fewer negative Jacobian determinants compared to SuPReMo.

CONCLUSIONS

Unlike conventional 4DCT reconstruction techniques that ignore breath inter-cycle motion variations, the proposed model computes both intra-cycle and inter-cycle motions. It represents motion over an extended timeframe, covering several minutes of kV scan series.

Early outcomes of MR-guided SBRT for patients with recurrent pancreatic adenocarcinoma

BACKGROUND

Local treatment options for locally recurrent pancreatic adenocarcinoma (LR-PAC) are limited, with median survival time (MST) of 9-13 months (mos) following recurrence. MRI-guided stereotactic body radiation therapy (MRgSBRT) provides the ability to dose escalate while sparing normal tissue. Here we report on the early outcomes of MRgSBRT for LR-PAC.

METHODS

Patients with prior resection of pancreatic adenocarcinoma with local recurrence treated with MRgSBRT at a single tertiary referral center from 5-2021 to 2-2023 were identified from our prospective database. MRgSBRT was delivered to 40-50 Gy in 4-5 fractions with target and OAR delineation per institutional standards. Endpoints included local control per RECIST v1.1, distant failure, overall survival (OS), and acute and chronic toxicities per Common Terminology Criteria for Adverse Events, v5.

RESULTS

Fifteen patients with LR-PAC were identified with median follow-up of 10.6 mos (2.8-26.5 mos) from MRgSBRT. There were 8 females and 7 males, with a median age of 69 years (50-83). One patient underwent neoadjuvant radiation for 50.4 Gy in 28 fractions followed by resection, and one underwent adjuvant radiation for 45 Gy in 25 fractions prior to recurrence. MRgSBRT was delivered a median of 18.8 mos (3.5-52.8 mos) following resection. OS following recurrence at 6 and 12 mos were 87% and 51%, respectively, with a median survival time of 14.1 mos (3.2-27.4 mos). Three patients experienced local failure at 5.9, 7.8, and 16.6 months from MgSBRT with local control of 92.3% and 83.9% at 6 and 12 months. 10 patients experienced distant failure at a median of 2.9 mos (0.3-6.7 mos). Grade 1-2 acute GI toxicity was noted in 47% of patients, and chronic GI toxicity in 31% of patients. No grade > 3 toxicities were noted.

CONCLUSIONS

This is the first report on toxicity and outcomes of MRgSBRT for LR-PAC in the literature. MRgSBRT is a safe, feasible treatment modality with the potential for improved local control in this vulnerable population. Future research is necessary to better identify which patients yield the most benefit from MRgSBRT, which should continue to be used with systemic therapy as tolerated.

TRIAL REGISTRATION

Jefferson IRB#20976, approved 2/17/21.

Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence

Introduction

Patient selection remains challenging as the clinical use of re-irradiation (re-RT) increases. Re-RT data is limited to retrospective studies and small prospective single-institution reports, resulting in small, heterogenous data sets. Validated prognostic and predictive biomarkers are derived from large-volume studies with long-term follow-up. This review aims to examine existing re-RT publications and available data sets and discuss strategies using artificial intelligence (AI) to approach small data sets to optimize the use of re-RT data.

Methods

Re-RT publications were identified where associated public data was present. The existing literature on small data sets to identify biomarkers was also explored.

Results

Publications with associated public data were identified, with glioma and nasopharyngeal cancers emerging as the most common tumor sites where the use of re-RT was the primary management approach. Existing and emerging AI strategies have been used to approach small data sets including data generation, augmentation, discovery, and transfer learning.

Conclusions

Further data is needed to generate adaptive frameworks, improve the collection of specimens for molecular analysis, and improve the interpretability of results in re-RT data.

MR-LINAC, a New Partner in Radiation Oncology: Current Landscape

Technological advances in radiation oncology are oriented towards improving treatment precision and tumor control. Among these advances, magnetic-resonance-image-guided radiation therapy (MRgRT) stands out, with technological advances to deliver targeted treatments adapted to a tumor's anatomy on the day while minimizing incidental exposure to organs at risk, offering an unprecedented therapeutic advantage compared to X-ray-based IGRT delivery systems. This new technology changes the traditional workflow in radiation oncology and requires an evolution in team coordination to administer more precise treatments. Once implemented, it paves the way for newer indication for radiation therapy to safely deliver higher doses than ever before, with better preservation of healthy tissues to optimize patient outcomes. In this narrative review, we assess the technical aspects of the novel linear accelerators that can deliver MRgRT and summarize the available published experience to date, focusing on oncological results and future challenges.

MRI-Guided Radiation Therapy-An Emerging and Disruptive Process of Care: Healthcare Economic and Policy Considerations

MRI-guided radiation therapy (MRgRT) is an emerging, innovative technology that provides opportunities to transform and improve the current clinical care process in radiation oncology. As with many new technologies in radiation oncology, careful evaluation from a healthcare economic and policy perspective is required for its successful implementation. In this review article, we describe the current evidence surrounding MRgRT, framing it within the context of value within the healthcare system. Additionally, we highlight areas in which MRgRT may disrupt the current process of care, and discuss the evidence thresholds and timeline required for the widespread adoption of this promising technology.

Determination of acceptable Hounsfield units uncertainties via a sensitivity analysis for an accurate dose calculation in the context of prostate MRI-only radiotherapy

Radiation therapy is moving from CT based to MRI guided planning, particularly for soft tissue anatomy. An important requirement of this new workflow is the generation of synthetic-CT (sCT) from MRI to enable treatment dose calculations. Automatic methods to determine the acceptable range of CT Hounsfield Unit (HU) uncertainties to avoid dose distribution errors is thus a key step toward safe MRI-only radiotherapy. This work has analysed the effects of controlled errors introduced in CT scans on the delivered radiation dose for prostate cancer patients. Spearman correlation coefficient has been computed, and a global sensitivity analysis performed following the Morris screening method. This allows the classification of different error factors according to their impact on the dose at the isocentre. sCT HU estimation errors in the bladder appeared to be the least influential factor, and sCT quality assessment should not only focus on organs surrounding the radiation target, as errors in other soft tissue may significantly impact the dose in the target volume. This methodology links dose and intensity-based metrics, and is the first step to define a threshold of acceptability of HU uncertainties for accurate dose planning.

Recent Advances and Adaptive Strategies in Image Guidance for Cervical Cancer Radiotherapy

The standard of care for locally advanced cervical cancer is external beam radiotherapy (EBRT) with simultaneous chemotherapy followed by an internal radiation boost. New imaging methods such as positron-emission tomography and magnetic resonance imaging have been implemented into daily practice for better tumor delineation in radiotherapy planning. The method of delivering radiation has changed with technical advances in qualitative imaging and treatment delivery. Image-guided radiotherapy (IGRT) plays an important role in minimizing treatment toxicity of pelvic radiation and provides a superior conformality for sparing the organs at risk (OARs) such as bone marrow, bowel, rectum, and bladder. Similarly, three-dimensional image-guided adaptive brachytherapy (3D-IGABT) with computed tomography (CT) or magnetic resonance imaging (MRI) has been reported to improve target coverage and reduce the dose to normal tissues. Brachytherapy is a complementary part of radiotherapy treatment for cervical cancer and, over the past 20 years, 3D-image-based brachytherapy has rapidly evolved and established itself as the gold standard. With new techniques and adaptive treatment in cervical cancer, the concept of personalized medicine is introduced with an enhanced comprehension of the therapeutic index not only in terms of volume (three-dimensional) but during treatment too (four-dimensional). Current data show promising results with integrated IGRT and IGABT in clinical practice and, therefore, better local control and overall survival while reducing treatment-related morbidity. This review gives an overview of the substantial impact that occurred in the progress of image-guided adaptive external beam radiotherapy and brachytherapy.