Motion Challenge of Thoracic Tumors at Radiotherapy by Introducing an Available Compensation Strategy

In this chapter a description is explained about radiotherapy as common available method in treatment of thoracic tumors located at thorax region of patient body and move mainly due to respiration. In radiotherapy of dynamic tumors, the correct and accurate information of tumor position during the therapeutic irradiation determine the degree of treatment success. In this chapter we investigate quantitatively the effect of tumor motion on treatment quality by considering to possible drawbacks and errors at external surrogate’s radiotherapy as clinical treatment modality. For this aim, tumor motion information of a group of real patients treated with Cybeknife Synchrony system (from Georgetown University Hospital) was taken into account. A fuzzy logic based correlation model was employed for tumor motion tracking. Final results represent graphically the amount of tumor motion estimated by our utilized correlation model on three dimensions with targeting error calculation. It’s worth mentioning that each strategy that can improve targeting accuracy of dynamic tumors may strongly enhance treatment quality by saving healthy tissues against additional high dose. In this chapter we just tried to introduce readers with thoracic tumor motion error as challenging issue in radiotherapy and motion compensation solutions, implemented clinically up to now

The Future of MR-Guided Radiation Therapy

Magnetic resonance image guided radiation therapy (MRIgRT) is a relatively new technology that has already shown outcomes benefits but that has not yet reached its clinical potential. The improved soft-tissue contrast provided with MR, coupled with the immediacy of image acquisition with respect to the treatment, enables expansion of on-table adaptive protocols, currently at a cost of increased treatment complexity, use of human resources, and longer treatment slot times, which translate to decreased throughput. Many approaches are being investigated to meet these challenges, including the development of artificial intelligence (AI) algorithms to accelerate and automate much of the workflow and improved technology that parallelizes workflow tasks, as well as improvements in image acquisition speed and quality. This article summarizes limitations of current available integrated MRIgRT systems and gives an outlook about scientific developments to further expand the use of MRIgRT

Artificial Intelligence-based Motion Tracking in Cancer Radiotherapy: A Review

Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body radiotherapy (SBRT), and proton therapy have been developed to deliver doses more precisely to the target. While such technologies have improved dose delivery, the implementation of intra-fraction motion management to verify tumor position at the time of treatment has become increasingly relevant. Recently, artificial intelligence (AI) has demonstrated great potential for real-time tracking of tumors during treatment. However, AI-based motion management faces several challenges including bias in training data, poor transparency, difficult data collection, complex workflows and quality assurance, and limited sample sizes. This review serves to present the AI algorithms used for chest, abdomen, and pelvic tumor motion management/tracking for radiotherapy and provide a literature summary on the topic. We will also discuss the limitations of these algorithms and propose potential improvements.Comment: 36 pages, 5 Figures, 4 Table

Medical physics challenges in clinical MR-guided radiotherapy

The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization

Real-time intrafraction motion monitoring in external beam radiotherapy

© 2019 Institute of Physics and Engineering in Medicine. Radiotherapy (RT) aims to deliver a spatially conformal dose of radiation to tumours while maximizing the dose sparing to healthy tissues. However, the internal patient anatomy is constantly moving due to respiratory, cardiac, gastrointestinal and urinary activity. The long term goal of the RT community to 'see what we treat, as we treat' and to act on this information instantaneously has resulted in rapid technological innovation. Specialized treatment machines, such as robotic or gimbal-steered linear accelerators (linac) with in-room imaging suites, have been developed specifically for real-time treatment adaptation. Additional equipment, such as stereoscopic kilovoltage (kV) imaging, ultrasound transducers and electromagnetic transponders, has been developed for intrafraction motion monitoring on conventional linacs. Magnetic resonance imaging (MRI) has been integrated with cobalt treatment units and more recently with linacs. In addition to hardware innovation, software development has played a substantial role in the development of motion monitoring methods based on respiratory motion surrogates and planar kV or Megavoltage (MV) imaging that is available on standard equipped linacs. In this paper, we review and compare the different intrafraction motion monitoring methods proposed in the literature and demonstrated in real-time on clinical data as well as their possible future developments. We then discuss general considerations on validation and quality assurance for clinical implementation. Besides photon RT, particle therapy is increasingly used to treat moving targets. However, transferring motion monitoring technologies from linacs to particle beam lines presents substantial challenges. Lessons learned from the implementation of real-time intrafraction monitoring for photon RT will be used as a basis to discuss the implementation of these methods for particle RT

マーカーレス腫瘍位置決めを目的とした深層学習に基づく患者固有標的輪郭予測モデルの開発

京都大学新制・課程博士博士(人間健康科学)甲第24542号人健博第113号新制||人健||8(附属図書館)京都大学大学院医学研究科人間健康科学系専攻(主査)教授 中尾 恵, 教授 杉本 直三, 教授 黒田 知宏学位規則第4条第1項該当Doctor of Human Health SciencesKyoto UniversityDFA

Translational Research of Audiovisual Biofeedback: An investigation of respiratory-guidance in lung and liver cancer patient radiation therapy

Through the act of breathing, thoracic and abdominal anatomy is in constant motion and is typically irregular. This irregular motion can exacerbate errors in radiation therapy, breathing guidance interventions operate to minimise these errors. However, much of the breathing guidance investigations have not directly quantified the impact of regular breathing on radiation therapy accuracy. The first aim of this thesis was to critically appraise the literature in terms of the use of breathing guidance interventions via systematic review. This review found that 21 of the 27 identified studies yielded significant improvements from the use of breathing guidance. None of the studies were randomised and no studies quantified the impact on 4DCT image quality. The second aim of this thesis was to quantify the impact of audiovisual biofeedback breathing guidance on 4DCT. This study utilised data from an MRI study to program the motion of a digital phantom prior to then simulating 4DCT imaging. Audiovisual biofeedback demonstrated to significantly improved 4DCT image quality over free breathing. The third aim of this thesis was to assess the impact of audiovisual biofeedback on liver cancer patient breathing over a course of stereotactic body radiation therapy (SBRT). The findings of this study demonstrated the effectiveness of audiovisual biofeedback in producing consistent interfraction respiratory motion over a course of SBRT. The fourth aim of this thesis was to design and implement a phase II clinical trial investigating the use and impact of audiovisual biofeedback in lung cancer radiation therapy. The findings of a retrospective analysis were utilised to design and determine the statistics of the most comprehensive breathing guidance study to date: a randomised, stratified, multi-site, phase II clinical trial.. The fifth aim of this thesis was to explore the next stages of audiovisual biofeedback in terms of translating evidence into broader clinical use through commercialisation. This aim was achieved by investigating the the product-market fit of the audiovisual biofeedback technology. The culmination of these findings demonstrates the clinical benefit of the audiovisual biofeedback respiratory guidance system and the possibility to make breathing guidance systems more widely available to patients

Artificial General Intelligence for Radiation Oncology

The emergence of artificial general intelligence (AGI) is transforming radiation oncology. As prominent vanguards of AGI, large language models (LLMs) such as GPT-4 and PaLM 2 can process extensive texts and large vision models (LVMs) such as the Segment Anything Model (SAM) can process extensive imaging data to enhance the efficiency and precision of radiation therapy. This paper explores full-spectrum applications of AGI across radiation oncology including initial consultation, simulation, treatment planning, treatment delivery, treatment verification, and patient follow-up. The fusion of vision data with LLMs also creates powerful multimodal models that elucidate nuanced clinical patterns. Together, AGI promises to catalyze a shift towards data-driven, personalized radiation therapy. However, these models should complement human expertise and care. This paper provides an overview of how AGI can transform radiation oncology to elevate the standard of patient care in radiation oncology, with the key insight being AGI's ability to exploit multimodal clinical data at scale