OBJECTIVE: Positron emission tomography (PET) imaging has been useful in delineating tumor volumes and allowing for improved radiation treatment. The field of PET-guided radiotherapy is rapidly growing and will have significant impact on radiotherapy delivery in the future. This narrative review provides an overview of the current state of PET-guided radiotherapy as well as the future directions of the field.
METHODS: For this narrative review, PubMed was searched for articles from 2010-2023. A total of 18 keywords or phrases were searched to provide an overview of PET-guided radiotherapy, radiotracers, the role of PET-guided radiotherapy in oligometastatic disease, and biology-guided radiotherapy (BgRT). The first 300 results for each keyword were searched and relevant articles were extracted. The references of these articles were also reviewed for relevant articles.
UNASSIGNED: In radiotherapy, 18F-2-fluoro-2-deoxy-D-glucose (F-FDG or FDG) is the major radiotracer for PET and when combined with computed tomography (CT) scan allows for anatomic visualization of metabolically active malignancy. Novel radiotracers are being explored to delineate certain cell types and numerous tumor metrics including metabolism, hypoxia, vascularity, and cellular proliferation. This molecular and functional imaging will provide improved tumor characterization. Through these radiotracers, radiation plans can employ dose painting by creating different dose levels based upon specific risk factors of the target volume. Additionally, biologic imaging during radiotherapy can allow for adaptation of the radiation plan based on response to treatment. Dose painting and adaptive radiotherapy should improve the therapeutic ratio through more selective dose delivery. The novel PET-linear accelerator hopes to combine these techniques and more by using radiotracers to deliver BgRT. The areas of radiotracer uptake will serve as fiducials to guide radiotherapy to themselves. This technique may prove promising in the growing area of oligometastatic radiation treatment.
CONCLUSIONS: Significant challenges exist for the future of PET-guided radiotherapy. However, with the advancements being made, PET imaging is set to change the delivery of radiotherapy.

BACKGROUND: Implementation of daily cone-beam CT (CBCT) into clinical practice in paediatric image-guided radiotherapy (IGRT) lags behind compared to adults. Surveys report wide variation in practice for paediatric IGRT and technical information remains unreported. In this study we report on technical settings from applied paediatric CBCT protocols and review the literature for paediatric CBCT protocols.
METHODS: From September to October 2022, a survey was conducted among 246 SIOPE-affiliated centres across 35 countries. The survey consisted of 3 parts: 1) baseline information; technical CBCT exposure settings and patient set-up procedure for 2) brain/head, and 3) abdomen. Descriptive statistics was used to summarise current practice. The literature was reviewed systematically with two reviewers obtaining consensus RESULTS: The literature search revealed 22 papers concerning paediatric CBCT protocols. Seven papers focused on dose-optimisation. Responses from 50/246 centres in 25/35 countries were collected: 44/50 treated with photons and 10/50 with protons. In total, 48 brain/head and 53 abdominal protocols were reported. 42/50 centres used kV-CBCT for brain/head and 35/50 for abdomen; daily CBCT was used for brain/head = 28/48 (58%) and abdomen = 33/53 62%. Greater consistency was seen in brain/head protocols (dose range 0.32 - 67.7 mGy) compared to abdominal (dose range 0.27 - 119.7 mGy).
CONCLUSIONS: Although daily CBCT is now widely used in paediatric IGRT, our survey demonstrates a wide range of technical settings, suggesting an unmet need to optimise paediatric IGRT protocols. This is in accordance with the literature. However, there are only few paediatric optimisation studies suggesting that dose reduction is possible while maintaining image quality.

MR-guided radiotherapy is a treatment approach that combines the advantages of magnetic resonance imaging (MRI) with the precision of radiation therapy. This practical review provides an overview of the current state-of-the-art of MR-guided radiotherapy for rectal cancer, including its technical aspects, clinical outcomes, and existing limitations. Even though some studies have demonstrated the feasibility and safety of this treatment modality, challenges remain in terms of patient selection, treatment planning optimization, and long-term follow-up. Despite these issues, MR-guided radiotherapy shows promise as a potentially valuable rectal cancer treatment approach.

UNASSIGNED: To evaluate the use of this new technique, surface-guided radiotherapy (SGRT), for patient setup and motion management in various cancers.
UNASSIGNED: Data was collected from 533 patients, who received treatment in our hospital for various malignancies using SGRT from October 2019 to April 2021. We studied patient setup, interfraction position, and patient position during the breath-hold (BH) technique. The main advantage of SGRT is that, it is completely non-invasive and uses visible light to compare the patient\'s skin surface in the treatment room and planned treatment position. In this analysis, Monaco 5.51.10 (Elekta) treatment planning system, Versa HD Linear Accelerator, and AlignRT 6.2 (Vision RT) SGRT system were used.
UNASSIGNED: With SGRT, treatment setup time can be reduced with more precision and techniques like Deep inspiration breathhold (DIBH) can be done with very good compliance.
UNASSIGNED: SGRT has shown improved accuracy in patient setup compared to conventional laser setup. The daily kilo voltage imaging frequency can be reduced; it helps in reducing additional radiation exposure due to imaging. SGRT has demonstrated reproducibility with adequate accuracy in BH treatments in DIBH for breast and SBRT.

BACKGROUND: The advent of image-guided radiation therapy (IGRT) has recently changed the workflow of radiation treatments by ensuring highly collimated treatments. Artificial intelligence (AI) and radiomics are tools that have shown promising results for diagnosis, treatment optimization and outcome prediction. This review aims to assess the impact of AI and radiomics on modern IGRT modalities in RT.
METHODS: A PubMed/MEDLINE and Embase systematic review was conducted to investigate the impact of radiomics and AI to modern IGRT modalities. The search strategy was \"Radiomics\" AND \"Cone Beam Computed Tomography\"; \"Radiomics\" AND \"Magnetic Resonance guided Radiotherapy\"; \"Radiomics\" AND \"on board Magnetic Resonance Radiotherapy\"; \"Artificial Intelligence\" AND \"Cone Beam Computed Tomography\"; \"Artificial Intelligence\" AND \"Magnetic Resonance guided Radiotherapy\"; \"Artificial Intelligence\" AND \"on board Magnetic Resonance Radiotherapy\" and only original articles up to 01.11.2022 were considered.
RESULTS: A total of 402 studies were obtained using the previously mentioned search strategy on PubMed and Embase. The analysis was performed on a total of 84 papers obtained following the complete selection process. Radiomics application to IGRT was analyzed in 23 papers, while a total 61 papers were focused on the impact of AI on IGRT techniques.
CONCLUSIONS: AI and radiomics seem to significantly impact IGRT in all the phases of RT workflow, even if the evidence in the literature is based on retrospective data. Further studies are needed to confirm these tools\' potential and provide a stronger correlation with clinical outcomes and gold-standard treatment strategies.

Adaptive radiation therapy is a feedback process by which imaging information acquired over the course of treatment, such as changes in patient anatomy, can be used to reoptimize the treatment plan, with the end goal of improving target coverage and reducing treatment toxicity. This review describes different types of adaptive radiation therapy and their clinical implementation with a focus on CT-guided online adaptive radiation therapy. Depending on local anatomic changes and clinical context, different anatomic sites and/or disease stages and presentations benefit from different adaptation strategies. Online adaptive radiation therapy, where images acquired in-room before each fraction are used to adjust the treatment plan while the patient remains on the treatment table, has emerged to address unpredictable anatomic changes between treatment fractions. Online treatment adaptation places unique pressures on the radiation therapy workflow, requiring high-quality daily imaging and rapid recontouring, replanning, plan review, and quality assurance. Generating a new plan with every fraction is resource intensive and time sensitive, emphasizing the need for workflow efficiency and clinical resource allocation. Cone-beam CT is widely used for image-guided radiation therapy, so implementing cone-beam CT-guided online adaptive radiation therapy can be easily integrated into the radiation therapy workflow and potentially allow for rapid imaging and replanning. The major challenge of this approach is the reduced image quality due to poor resolution, scatter, and artifacts. Keywords: Adaptive Radiation Therapy, Cone-Beam CT, Organs at Risk, Oncology © RSNA, 2023.

In recent years, proton therapy centres have begun to shift from conventional 2D-kV imaging to volumetric imaging systems for image guided proton therapy (IGPT). This is likely due to the increased commercial interest and availability of volumetric imaging systems, as well as the shift from passively scattered proton therapy to intensity modulated proton therapy. Currently, there is no standard modality for volumetric IGPT, leading to variation between different proton therapy centres. This article reviews the reported clinical use of volumetric IGPT, as available in published literature, and summarises their utilisation and workflow where possible. In addition, novel volumetric imaging systems are also briefly summarised highlighting their potential benefits for IGPT and the challenges that need to be overcome before they can be used clinically.

OBJECTIVE: Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof-of-concept clinical implementation of an AI-assisted review of CBCT registrations used for patient setup.
METHODS: An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45-day period, 1357 pre-treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day\'s registrations were produced. Initial action levels targeted 10% of cases to highlight for in-depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI-model performance.
RESULTS: Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.
CONCLUSIONS: In this work, we describe the implementation of an automated AI-analysis pipeline for daily quantitative analysis of CBCT-guided patient setup registrations. The AI-model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors\' knowledge, there are no previous works performing AI-assisted assessment of pre-treatment CBCT-based patient alignment.

Recent years have seen both a fresh knowledge of cancer and impressive advancements in its treatment. However, the clinical treatment paradigm of cancer is still difficult to implement in the twenty-first century due to the rise in its prevalence. Radiotherapy (RT) is a crucial component of cancer treatment that is helpful for almost all cancer types. The accuracy of RT dosage delivery is increasing as a result of the quick development of computer and imaging technology. The use of image-guided radiation (IGRT) has improved cancer outcomes and decreased toxicity. Online adaptive radiotherapy will be made possible by magnetic resonance imaging-guided radiotherapy (MRgRT) using a magnetic resonance linear accelerator (MR-Linac), which will enhance the visibility of malignancies. This review\'s objectives are to examine the benefits of MR-Linac as a treatment approach from the perspective of various cancer patients\' prognoses and to suggest prospective development areas for additional study.

Radiation therapy plays an important role in tumor treatment. The development of image-guided radiation therapy (IGRT) technology provides a strong guarantee for precise radiation therapy of tumors. However, bibliometric studies on IGRT research have rarely been reported. This study uses literature collected from the Web of Science during 1987 to 2021 as a sample and uses the bibliometric method to reveal the current research status, hotspots, and development trends in IGRT. Based on 6407 papers published from the Web of Science during 1987 to 2021, we utilized Microsoft Excel 2007 and cite space software to perform statistical analysis and visualization of IGRT. A total of 6407 articles were included, this area of IGRT has gone through 4 stages: budding period, growth period, outbreak period, and stationary period. The research category is mainly distributed in Radiology Nuclear Medicine Medical Imaging, which intersects with the research categories of Materials, Physics, and Mathematics. Yin FF, Tanderup K, and Sonke JJ are highly productive scholars who are active in IGRT research, while Jaffray DA, van Herk M and Guckenberger M are authors with high impact in this field. The team of scholars has close cooperation within the team and weak cooperation among teams. The League of European Research Universities, University of Texas System, University of Toronto, and Princess Margaret Cancer are the main research institutions in this field. The United States has the most research literature, followed by China and Germany. Six thousand four hundred seven articles are distributed in 712 journals, and the top 3 journals are Med Phys, Int J Radiat Oncol, and Radiather Oncol. Precise registration, intelligence, magnetic resonance guidance, and deep learning are current research hotspots. These results demonstrate that the research in this field is relatively mature and fruitful in the past 35 years, providing a solid theoretical basis and practical experience for precision radiotherapy.