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R-IDEAL:放射肿瘤学技术创新系统临床评估框架

R-IDEAL: A Framework for Systematic Clinical Evaluation of Technical Innovations in Radiation Oncology.

作者信息

Verkooijen Helena M, Kerkmeijer Linda G W, Fuller Clifton D, Huddart Robbert, Faivre-Finn Corinne, Verheij Marcel, Mook Stella, Sahgal Arjun, Hall Emma, Schultz Chris

机构信息

Imaging Division, University Medical Center Utrecht, Utrecht, Netherlands.

Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.

出版信息

Front Oncol. 2017 Apr 3;7:59. doi: 10.3389/fonc.2017.00059. eCollection 2017.

Abstract

The pace of innovation in radiation oncology is high and the window of opportunity for evaluation narrow. Financial incentives, industry pressure, and patients' demand for high-tech treatments have led to widespread implementation of innovations before, or even without, robust evidence of improved outcomes has been generated. The standard phase I-IV framework for drug evaluation is not the most efficient and desirable framework for assessment of technological innovations. In order to provide a standard assessment methodology for clinical evaluation of innovations in radiotherapy, we adapted the surgical IDEAL framework to fit the radiation oncology setting. Like surgery, clinical evaluation of innovations in radiation oncology is complicated by continuous technical development, team and operator dependence, and differences in quality control. Contrary to surgery, radiotherapy innovations may be used in various ways, e.g., at different tumor sites and with different aims, such as radiation volume reduction and dose escalation. Also, the effect of radiation treatment can be modeled, allowing better prediction of potential benefits and improved patient selection. Key distinctive features of R-IDEAL include the important role of predicate and modeling studies (Stage 0), randomization at an early stage in the development of the technology, and long-term follow-up for late toxicity. We implemented R-IDEAL for clinical evaluation of a recent innovation in radiation oncology, the MRI-guided linear accelerator (MR-Linac). MR-Linac combines a radiotherapy linear accelerator with a 1.5-T MRI, aiming for improved targeting, dose escalation, and margin reduction, and is expected to increase the use of hypofractionation, improve tumor control, leading to higher cure rates and less toxicity. An international consortium, with participants from seven large cancer institutes from Europe and North America, has adopted the R-IDEAL framework to work toward coordinated, evidence-based introduction of the MR-Linac. R-IDEAL holds the promise for timely, evidence-based introduction of radiotherapy innovations with proven superior effectiveness, while preventing unnecessary exposure of patients to potentially harmful interventions.

摘要

放射肿瘤学的创新步伐很快,评估的机会窗口很窄。经济激励、行业压力以及患者对高科技治疗的需求,导致在产生强有力的疗效改善证据之前,甚至在没有此类证据的情况下,创新就已广泛应用。药物评估的标准I-IV期框架并非评估技术创新的最有效和理想框架。为了为放射治疗创新的临床评估提供标准评估方法,我们对手术IDEAL框架进行了调整,以适应放射肿瘤学环境。与手术一样,放射肿瘤学创新的临床评估因技术不断发展、对团队和操作人员的依赖以及质量控制差异而变得复杂。与手术不同的是,放射治疗创新可能有多种使用方式,例如,用于不同的肿瘤部位,具有不同的目的,如减少放疗体积和增加剂量。此外,可以对放射治疗的效果进行建模,从而更好地预测潜在益处并改善患者选择。R-IDEAL的关键显著特征包括前期研究和建模研究(0期)的重要作用、在技术开发早期进行随机分组以及对晚期毒性进行长期随访。我们将R-IDEAL应用于放射肿瘤学一项最新创新——磁共振成像引导直线加速器(MR-Linac)的临床评估。MR-Linac将放射治疗直线加速器与1.5-T磁共振成像相结合,旨在改善靶向性、增加剂量和缩小边界,预计会增加大分割放疗的使用,改善肿瘤控制,从而提高治愈率并降低毒性。一个由来自欧洲和北美的七家大型癌症研究所的人员组成的国际联盟,已采用R-IDEAL框架,致力于以协调一致、基于证据的方式引入MR-Linac。R-IDEAL有望及时、基于证据地引入经证实具有卓越疗效的放射治疗创新,同时防止患者不必要地暴露于潜在有害的干预措施。

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