摘要胃肠间质瘤(GIST)由于发病率较低,相关随机对照试验(RCT)常较难开展,而真实世界研究(RWS)可以通过整合真实世界数据,全面系统地分析临床诊疗决策与患者获益之间的关系,对疗效作出较好的评价,从而得到最优治疗方案;并且RWS通常可以进行较长时间的临床观察和随访,弥补了传统RCT的不足.基于此,GIST领域的RWS备受关注.目前GIST领域的临床RWS常围绕着GIST的诊治、预后以及临床预测等问题展开.GIST易误诊,常与其他间叶来源的肿瘤混淆,诊断方面的研究通常围绕某类诊断方法对GIST诊断的准确度以判断其应用价值;治疗性研究主要围绕某干预措施对GIST的疗效及不良反应;预后研究则是对GIST进展患者的预后状况进行客观描述,探究影响其预后的因素,并分析其生活质量;临床预测研究则旨在寻找GIST诊断或预后的最佳预测指标;RWS亦可以使用药物经济学评价的方法,评估不同干预措施对于GIST患者的成本效益,为GIST临床决策提供关于干预成本及健康产出的证据.在GIST领域的临床RWS设计中,应重视资料的来源和质量,加强与卫生统计学及临床流行病学专家的交流,多方协作、规范每个操作环节的实施步骤,才能开展高质量的RWS.

abstractsGastrointestinal stromal tumor (GIST) is a relatively rare type of gastrointestinal tract tumors. Thus, it is difficult to perform randomized controlled trials (RCTs) of GIST in a single center, which are often plagued by a small number of participants. Real world study (RWS) is a complement to the evidence derived from traditional RCT. Emerging sources of real world data offer enormous opportunity for deeper understanding of why treatments work (or not) and for whom. Evidence generated from RWD can help clarify best use of treatments for individuals and populations, and care value. Thus, RWS of GIST has attracted much attention. RWS helps us better understand the diagnosis, treatment, prognosis and outcome prediction of GIST in clinical practice. GIST is often misdiagnosed as other tumor. A diagnostic test provides evidence on how well a test correctly identifies or rules out GIST. Therapeutic research aims to evaluate the effectiveness and/or safety of a therapy for GIST. Prognostic research aims to forecast the likely outcome of GIST, explore the factors affecting the outcome, and analyze quality of life. Predictive research aims to quantify the probability of identification or health outcome of GIST based on a set of predictors. Pharmacoeconomic data in real world can evaluate the cost?effectiveness of medicinal products for GISTs and serve as a guidance tool for optimal healthcare resource allocation. Attaching importance to data sourse and data quality, strenthening communicate with a qualified statistician, and the implementation of a standardized process are necessary for performing a high?quality RWS.