Seminars in Radiation Oncology最新文献

The landscape of lung radiotherapy (RT) has rapidly evolved over the past decade with modern RT and surgical techniques, systemic therapies, and expanding indications for RT. To date, 2 MRI-guided RT (MRgRT) units, 1 using a 0.35T magnet and 1 using a 1.5T magnet, are available for commercial use with more systems in the pipeline. MRgRT offers distinct advantages such as real-time target tracking, margin reduction, and on-table treatment adaptation, which may help overcome many of the common challenges associated with thoracic RT. Nonetheless, the use of MRI for image guidance and the current MRgRT units also have intrinsic limitations. In this review article, we will discuss clinical experiences to date, advantages, challenges, and future directions of MRgRT to the lung.

The recent introduction of a commercial 1.5 T MR-linac system has considerably improved the image quality of the patient acquired in the treatment unit as well as enabling online adaptive radiation therapy (oART) treatment strategies. Quality Assurance (QA) of this new technology requires new methodology that allows for the high field MR in a linac environment. The presence of the magnetic field requires special attention to the phantoms, detectors, and tools to perform QA. Due to the design of the system, the integrated megavoltage imager (MVI) is essential for radiation beam calibrations and QA. Additionally, the alignment between the MR image system and the radiation isocenter must be checked. The MR-linac system has vendor-supplied phantoms for calibration and QA tests. However, users have developed their own routine QA systems to independently check that the machine is performing as required, as to ensure we are able to deliver the intended dose with sufficient certainty. The aim of this work is therefore to review the MR-linac specific QA procedures reported in the literature.

The advent of MRI guided radiotherapy (MRIgRT) offers enormous promise in the treatment of prostate cancer. The MR-linac offers men the opportunity to receive daily MR imaging to guide and influence their radiotherapy treatment. This review focuses on the advantages that MRIgRT potentially offers as well as any potential disadvantages to MRIgRT that may have been recognized thus far. Ongoing clinical trials evaluating this novel treatment platform for the treatment of prostate cancer are also discussed.

Although magnetic resonance imaging (MRI) has become standard diagnostic workup for head and neck malignancies and is currently recommended by most radiological societies for pharyngeal and oral carcinomas, its utilization in radiotherapy has been heterogeneous during the last decades. However, few would argue that implementing MRI for annotation of target volumes and organs at risk provides several advantages, so that implementation of the modality for this purpose is widely accepted. Today, the term MR-guidance has received a much broader meaning, including MRI for adaptive treatments, MR-gating and tracking during radiotherapy application, MR-features as biomarkers and finally MR-only workflows. First studies on treatment of head and neck cancer on commercially available dedicated hybrid-platforms (MR-linacs), with distinct common features but also differences amongst them, have also been recently reported, as well as “biological adaptation” based on evaluation of early treatment response via functional MRI-sequences such as diffusion weighted ones. Yet, all of these approaches towards head and neck treatment remain at their infancy, especially when compared to other radiotherapy indications. Moreover, the lack of standardization for reporting MR-guided radiotherapy is a major obstacle both to further progress in the field and to conduct and compare clinical trials. Goals of this article is to present and explain all different aspects of MR-guidance for radiotherapy of head and neck cancer, summarize evidence, as well as possible advantages and challenges of the method and finally provide a comprehensive reporting guidance for use in clinical routine and trials.

Growing evidence has demonstrated significant, persistent, and widespread disparities in cancer clinical trial enrollment across myriad disease sites and target populations. Although mechanisms underlying such disparities are complex and multifactorial, clinical trial eligibility criteria may serve as a key structural barrier to equitable and diverse trial enrollment. In this review, we provide an overview of the data describing historical and current disparities in cancer clinical trial enrollment and subsequently describe several patient-, institution-, and trial-related factors which appear to be key drivers of enrollment inequity, with specific discussion regarding the impact of eligibility criteria. We further describe the landscape of ongoing professional efforts aimed at eliminating clinical trial disparities through various medical, professional, and advocacy groups. The review concludes with a practical discussion of how modernization of eligibility criteria in clinical trials may decrease or eliminate trial disparities, including specific actionable recommendations aimed at improving the quality of future eligibility criteria.

Advances in proton therapy have garnered much attention and speculation in recent years as the indications for proton therapy have grown beyond pediatric, prostate, spine, and ocular tumors. To achieve and maintain consistent access to this cancer treatment and to ensure the future viability and availability of proton centers in the United States, a call for evidence has been heard and answered by proton radiation oncologists. Answers provided in this review include the evolution of proton therapy research, rationale for proton clinical trial design, challenges in and barriers to the conduct of proton therapy research, and other unique considerations for the study of proton therapy.

The paradigm of oligometastatic disease (OMD), characterized by a limited number of metastases potentially amenable to local therapies, presents unique opportunities and challenges in clinical trial design and implementation. Although local ablative therapies, such as stereotactic body radiation therapy, have shown promise in improving outcomes for patients with OMD, there is a lack of large-scale randomized phase III trials supporting their widespread use. This paper outlines the key challenges in trial design and implementation in the oligometastatic setting, including appropriate patient selection, the definition of the oligometastatic state, trial design considerations, endpoint selection, and logistical considerations related to enrollment and follow-up. We suggest potential strategies to address these challenges, emphasizing the importance of a comprehensive, patient-centric approach, and the integration of multidisciplinary teams in trial design and implementation. The aim is to encourage the design of well-structured clinical trials, ultimately refining best practices and enhancing patient outcomes in the management of OMD.

The practice of oncology requires analyzing and synthesizing abundant data. From the patient's workup to determine eligibility to the therapies received to the post-treatment surveillance, practitioners must constantly juggle, evaluate, and weigh decision-making based on their best understanding of information at hand. These complex, multifactorial decisions have a tremendous opportunity to benefit from data-driven machine learning (ML) methods to drive opportunities in artificial intelligence (AI). Within the past 5 years, we have seen AI move from simply a promising opportunity to being used in prospective trials. Here, we review recent efforts of AI in clinical trials that have moved the needle towards improved prediction of actionable outcomes, such as predicting acute care visits, short term mortality, and pathologic extranodal extension. We then pause and reflect on how these AI models ask a different question than traditional statistics models that readers may be more familiar with; how then should readers conceptualize and interpret AI models that they are not as familiar with. We end with what we believe are promising future opportunities for AI in oncology, with an eye towards allowing the data to inform us through unsupervised learning and generative models, rather than asking AI to perform specific functions.

Clinical trials have been the center of progress in modern medicine. In oncology, we are fortunate to have a structure in place through the National Clinical Trials Network (NCTN). The NCTN provides the infrastructure and a forum for scientific discussion to develop clinical concepts for trial design. The NCTN also provides a network group structure to administer trials for successful trial management and outcome analyses. There are many important aspects to trial design and conduct. Modern trials need to ensure appropriate trial conduct and secure data management processes. Of equal importance is the quality assurance of a clinical trial. If progress is to be made in oncology clinical medicine, investigators and patient care providers of service need to feel secure that trial data is complete, accurate, and well-controlled in order to be confident in trial analysis and move trial outcome results into daily practice. As our technology has matured, so has our need to apply technology in a uniform manner for appropriate interpretation of trial outcomes. In this article, we review the importance of quality assurance in clinical trials involving radiation therapy. We will include important aspects of institution and investigator credentialing for participation as well as ongoing processes to ensure that each trial is being managed in a compliant manner. We will provide examples of the importance of complete datasets to ensure study interpretation. We will describe how successful strategies for quality assurance in the past will support new initiatives moving forward.