This review systematically summarizes the evidence on the economic impact of magnetic resonance image-guided RT (MRIgRT).
We systematically searched INAHTA, MEDLINE, and Scopus up to March 2022 to retrieve health economic studies. Relevant data were extracted on study type, model inputs, modeling methods and economic results.
Five studies were included. Two studies performed a full economic assessment to compare the cost-effectiveness of MRIgRT with other forms of image-guided radiation therapy. One study performed a cost minimization analysis and two studies performed an activity-based costing, all comparing MRIgRT with X-ray computed tomography image-guided radiation therapy (CTIgRT). Prostate cancer was the target condition in four studies and hepatocellular carcinoma in one. Considering the studies with a full economic assessment, MR-guided stereotactic body radiation therapy was found to be cost effective with respect to CTIgRT or conventional or moderate hypofractionated RT, even with a low reduction in toxicity. Conversely, a greater reduction in toxicity is required to compete with extreme hypofractionated RT without MR guidance.
This review highlights the great potential of MRIgRT but also the need for further evidence, especially for late toxicity, whose reduction is expected to be the real added value of this technology.

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the clinical workflow for the diagnosis and treatment planning of various diseases. Machine learning-based artificial intelligence (AI) methods, especially those adopting the deep learning technique, have been extensively employed to perform mpMRI image classification, segmentation, registration, detection, reconstruction, and super-resolution. The current availabilities of increasing computational power and fast-improving AI algorithms have empowered numerous computer-based systems for applying mpMRI to disease diagnosis, imaging-guided radiotherapy, patient risk and overall survival time prediction, and the development of advanced quantitative imaging technology for magnetic resonance fingerprinting. However, the wide application of these developed systems in the clinic is still limited by a number of factors, including robustness, reliability, and interpretability. This survey aims to provide an overview for new researchers in the field as well as radiologists with the hope that they can understand the general concepts, main application scenarios, and remaining challenges of AI in mpMRI.

In recent years, we have seen the incorporation of magnetic resonance imaging (MRI) simulators into radiotherapy centres and the emergence of the new technology of MR linacs. However, the significant health care resources associated with this advanced technology impact immediate widespread use and availability. There are currently limited studies to demonstrate the clinical effectiveness and inform decision-making on the use of MRI in radiotherapy. The objective of this scoping review is to identify and map the existing evidence surrounding the clinical implementation of MRI-guided radiotherapy in patients with breast cancer. It also aims to identify challenges and knowledge gaps in the literature.
We will perform a comprehensive search in MEDLINE and EMBASE databases from January 2010 onwards. Grey literature sources will include the WHO International Clinical Trials Registry Platform. We will include systematic reviews, randomised and non-randomised controlled studies published in English. Literature should examine the use of magnetic resonance imaging-guided radiotherapy in adults with breast cancer, regardless of cancer stage or severity. Two reviewers will independently screen all titles, abstracts and full-text reports. Data will be extracted and summarised using qualitative (e.g. content and thematic analysis) methods and presented in tables.
The results from this review will consolidate the evidence surrounding MRI-guided radiotherapy for breast cancer, contributing to the development and optimisation of patient selection, simulation, planning, treatment delivery, quality assurance and research, to help improve patient outcomes, cancer care and treatment for women with breast cancer.
The protocol is available on Open Science Framework at DOI https://doi.org/10.17605/OSF.IO/8TEV6.