Seminars in Radiation Oncology最新文献

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.

Optimal management of cancer patients relies heavily on late-phase oncology randomized controlled trials. A comprehensive understanding of the key considerations in designing and interpreting late-phase trials is crucial for improving subsequent trial design, execution, and clinical decision-making. In this review, we explore important aspects of late-phase oncology trial design. We begin by examining the selection of primary endpoints, including the advantages and disadvantages of using surrogate endpoints. We address the challenges involved in assessing tumor progression and discuss strategies to mitigate bias. We define informative censoring bias and its impact on trial results, including illustrative examples of scenarios that may lead to informative censoring. We highlight the traditional roles of the log-rank test and hazard ratio in survival analyses, along with their limitations in the presence of nonproportional hazards as well as an introduction to alternative survival estimands, such as restricted mean survival time or MaxCombo. We emphasize the distinctions between the design and interpretation of superiority and noninferiority trials, and compare Bayesian and frequentist statistical approaches. Finally, we discuss appropriate utilization of phase II and phase III trial results in shaping clinical management recommendations and evaluate the inherent risks and benefits associated with relying on phase II data for treatment decisions.

Underreporting of patient symptoms by clinicians is a common and well-documented phenomenon that has led to integrating patient-reported outcomes (PROs) as endpoints into clinical trials. While PROs are often used to measure disease symptoms, cancer therapy toxicities, and quality of life, they can also assess patients’ general experiences and preferences. With the increasing use of electronic medical records and the digital health revolution in oncology, conversion from paper to electronic PROs (ePROs) has also facilitated the integration of PROs into routine care. Evidence from clinical trials is rapidly emerging to support ePROs as a care delivery innovation, given the potential for ePROs to improve patient outcomes through timely evaluation and response to patient needs. Meanwhile, work is ongoing to understand and address ePRO use and challenges to equitable integration, including technical and language barriers for patients, clinicians, and health systems. Nonetheless, the health system and regulatory bodies continue to develop stipulations to promote the use of ePROs. Herein, we review the evolution of PROs from an endpoint to an intervention in prospective clinical trials in oncology.

Randomized controlled trials (RCTs) are the gold standard for comparative-effectiveness research (CER). Since the 1980s, there has been a rise in the creation and utilization of large national cancer databases to provide readily accessible “real-world data” (RWD). This review article discusses the role of RCTs in oncology, and the role of RWD from the national cancer database in CER. RCTs remain the preferred study type for CER because they minimize confounding and bias. RCTs have challenges to conduct, including extensive time and resources, but these factors do not impact the internal validity of the result. Generalizability and external validity are potential limitations of RCTs. RWD is ideal for studying cancer epidemiology, patterns of care, disparities in care delivery, quality-of-care evaluation, and applicability of RCT data in specific populations excluded from RCTs. However, retrospective databases with RWD have limitations in CER due to unmeasured confounders and are often suboptimal in identifying causal treatment effects.