In the present review, I provide an overview of the development of radiation therapy and short history of the Department of Radiation Oncology, Juntendo University. I also emphasize the importance of radiation therapy as a major treatment modality for cancers. Radiation therapy is a standard treatment for malignant tumors. It aims to deliver a sufficient radiation dose to a target volume to eradicate tumor cells or relieve symptoms of disease. Therapy can achieve good results in many types of cancers. Although radiation therapy sometimes causes undesirable adverse events, it is generally less invasive than other treatment modalities and does not alter the shape and function of healthy organs. When the author joined this field in 1981, radiation therapy techniques were highly primitive; however, during the past 42 years, treatment has advanced rapidly with the development of computer science, mechanical techniques and instrumentation. Currently, patients can be treated with precise radiation techniques, including intensity-modulated radiation therapy, image-guided radiation therapy, stereotactic irradiation, and brachytherapy. We also introduced a new treatment planning system that uses not only anatomical but also metabolic imaging, which permits correct delineation of the target volume. Therefore, it is crucial to stay up to date with advances and developments in rapidly emerging technologies to maintain high-quality treatment. The Department of Radiation Oncology at Juntendo University (Tokyo, Japan) is still small; however, it is gradually expanding and conducting research in both clinical and basic fields. It is the author\'s hope that many young investigators will join this field in the future.

Accurate information on set-up error during radiotherapy is essential for determining the optimal number of treatments in hypofractionated radiotherapy for prostate cancer. This necessitates careful control by the radiotherapy staff to assess the patient\'s condition. This study aimed to develop an evaluation method of the temporal trends in a patient\'s specific prostate movement during treatment using image matching and margin values. This study included 65 patients who underwent prostate volumetric modulated arc therapy (mean treatment time, 87.2 s). Set-up errors were assessed using bone, inter-, and intra-fraction marker matching across 39 fractions. The set-up margin was determined by dividing the four periods into 39 fractions using Stroom\'s formula and correlation coefficient. The intra-fraction set-up error was biased in the anterior-superior (AS) direction during treatment. The temporal trend of set-up errors during radiotherapy slightly increased based on bone matching and inter-fraction marker matching, with a 1.6-mm difference in the set-up margin fractions 11 to 20. The correlation coefficient of the mean prostate movement during treatment significantly decreased in the superior-inferior direction, while remaining high in the left-right and anterior-posterior directions. Image matching contributed significantly to the improvement of set-up errors; however, careful attention is needed for prostate movement in the AS direction, particularly during short treatment times. Understanding the trend of set-up errors during the treatment period is essential in numerical information sharing on patient condition and evaluating the margins for tailored hypo-fractionated radiotherapy, considering the facility\'s image-guided radiation therapy technology.

Objective.The aim of this study was to reconstruct volumetric computed tomography (CT) images in real-time from ultra-sparse two-dimensional x-ray projections, facilitating easier navigation and positioning during image-guided radiation therapy.Approach.Our approach leverages a voxel-sapce-searching Transformer model to overcome the limitations of conventional CT reconstruction techniques, which require extensive x-ray projections and lead to high radiation doses and equipment constraints.Main results.The proposed XTransCT algorithm demonstrated superior performance in terms of image quality, structural accuracy, and generalizability across different datasets, including a hospital set of 50 patients, the large-scale public LIDC-IDRI dataset, and the LNDb dataset for cross-validation. Notably, the algorithm achieved an approximately 300% improvement in reconstruction speed, with a rate of 44 ms per 3D image reconstruction compared to former 3D convolution-based methods.Significance.The XTransCT architecture has the potential to impact clinical practice by providing high-quality CT images faster and with substantially reduced radiation exposure for patients. The model\'s generalizability suggests it has the potential applicable in various healthcare settings.

OBJECTIVE: Respiratory motion-induced displacement of internal organs poses a significant challenge in image-guided radiation therapy, particularly affecting liver landmark tracking accuracy.
METHODS: Addressing this concern, we propose a self-supervised method for robust landmark tracking in long liver ultrasound sequences. Our approach leverages a Siamese-based context-aware correlation filter network, trained by using the consistency loss between forward tracking and back verification. By effectively utilizing both labeled and unlabeled liver ultrasound images, our model, Siam-CCF , mitigates the impact of speckle noise and artifacts on ultrasonic image tracking by a context-aware correlation filter. Additionally, a fusion strategy for template patch feature helps the tracker to obtain rich appearance information around the point-landmark.
RESULTS: Siam-CCF achieves a mean tracking error of 0.79 ± 0.83 mm at a frame rate of 118.6 fps, exhibiting a superior speed-accuracy trade-off on the public MICCAI 2015 Challenge on Liver Ultrasound Tracking (CLUST2015) 2D dataset. This performance won the 5th place on the CLUST2015 2D point-landmark tracking task.
CONCLUSIONS: Extensive experiments validate the effectiveness of our proposed approach, establishing it as one of the top-performing techniques on the CLUST2015 online leaderboard at the time of this submission.

OBJECTIVE: We created a phantom and analysis program for the assessment of IGRT positional accuracy. We verified the accuracy of analysis and the practicality of this evaluation method at several facilities.
METHODS: End-to-end test was performed using an in-house phantom, and EPID images were acquired after displacement by an arbitrary amount using a micrometer, with after image registration as the reference. The difference between the center of the target and the irradiated field was calculated using our in-house analysis program and commercial software. The end-to-end test was conducted at three facilities, and the IGRT positional accuracy evaluation was verified.
RESULTS: The maximum difference between the displacement of the target determined from the EPID image and the arbitrary amount of micrometer displacement was 0.24 mm for the in-house analysis program and 0.30 mm for the commercial software. The maximum difference between the center of the target and the irradiation field on EPID images acquired at the three facilities was 0.97 mm.
CONCLUSIONS: The proposed evaluation method using our in-house phantom and analysis program can be used for the assessment of IGRT positional accuracy.

Intraoperative radiation therapy (IORT) is a radiation technique applying a single fraction with a high dose during surgery. We report the first abdomino-pelvic application of an image-guided intraoperative electron radiation therapy with intraoperative real time dose calculation based on the individual intraoperative patient anatomy. A patient suffering from locoregionally recurrent rectal cancer after treatment with neoadjuvant re-chemoradiation was chosen for this approach. After surgical removal of the recurrence, an adequate IORT applicator was placed as usual. A novel mobile imaging device (ImagingRing, MedPhoton) was positioned around the patient covering the region to be treated with the IORT-applicator in place. It allowed the acquisition of three-dimensional intraoperative cone-beam computed tomography images suitable for dose calculation using an automated scaling (heuristic object and head scatter as well as hardening corrections) of Hounsfield units. After image acquisition confirmed the correct applicator position, the images were transferred to our treatment planning system for intraoperative dose calculation. Treatment could be accomplished using the calculated dose distribution. We herein describe the details of the procedure including necessary adjustments in the typically used IORT equipment and work flow. We further discuss the pros and cons of this new approach generally overcoming a decade long limitation of IORT procedures as well as future perspectives regarding IORT treatments.

Image-guided radiation therapy (IGRT) platforms for preclinical research represent an important advance for radiation research. IGRT-based platforms more accurately model the delivery of therapeutic ionizing radiation as delivered in clinical practice which permits more translationally and clinically relevant radiation biology research. Fundamentally, IGRT allows for precise delivery of ionizing radiation in order to (1) ensure that the tumor and/or target of interest is adequately covered by the prescribed radiation dose, and (2) to minimize the radiation dose delivered to adjacent nontargeted or normal tissues. Here, we describe the techniques and outline a general workflow employed for IGRT in preclinical in vivo tumor models.

BACKGROUND: Proton therapy is an effective treatment for ocular melanoma, and other tumors of the eye. The fixed horizontal beamline dedicated to ocular treatments at Massachusetts General Hospital was originally commissioned in 2002, with much of the equipment, safety features, and practices dating back to an earlier implementation at Harvard Cyclotron in the 1970s.
OBJECTIVE: To describe the experience of reevaluation and enhancement of the safety environment for one of the longest continuously operating proton therapy programs.
METHODS: Several enhancements in quality control had been introduced throughout the years of operation, as described in this manuscript, to better align the practice with the evolving standards of proton therapy and the demands of a modern hospital. We spotlight the design and results of the failure mode and effect analysis (FMEA), and subsequent actions introduced to mitigate the modes associated with elevated risk. The findings of the FMEA informed the specifications for the new software application, which facilitated the improved management of the treatment workflow and the image-guidance aspects of ocular treatments.
RESULTS: Eleven failure modes identified as having the highest risk are described. Six of these were mitigated with the clinical roll-out of a new application for image-guided radiation therapy (IGRT). Others were addressed through task automation, the broader introduction of checklists, and enhancements in pre-treatment staff-led time-out.
CONCLUSIONS: Throughout the task of modernizing the safety system of our dedicated ocular beamline, FMEA proved to be an effective instrument in soliciting inputs from the staff about safety and workflow concerns, helping to identify steps associated with elevated failure risks. Risks were reduced with the clinical introduction of a new IGRT application, which integrates quality management tools widely recognized for their role in risk mitigation: automation of the data transfer and workflow steps, and with the introduction of checklists and redundancy cross-checks.

A recent approach to radiotherapy for prostate cancer is the administration of high doses of radiation to the prostate while minimizing the risk of side effects. Thus, image-guided radiotherapy utilizes advanced imaging techniques and is a feasible strategy for increasing the radiation dose. New radioactive particles are another approach to achieving high doses and safe procedures. Prostate brachytherapy is currently considered as a combination therapy. Spacers are useful to protect adjacent organs, specifically the rectum, from excessive radiation exposure.

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.