Huang Erich P, Lin Frank I, Shankar Lalitha K
Biometric Research Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, 9609 Medical Center Drive, MSC 9735, Bethesda, MD 20892-9735.
Cancer Imaging Program, Division of Cancer Treatment, Diagnosis National Cancer Institute, NIH, Bethesda, Maryland.
Acad Radiol. 2017 Aug;24(8):1036-1049. doi: 10.1016/j.acra.2017.03.002. Epub 2017 Apr 26.
Despite the widespread belief that advanced imaging should be very helpful in guiding oncology treatment decision and improving efficiency and success rates in treatment clinical trials, its acceptance has been slow. Part of this is likely attributable to gaps in study design and statistical methodology for these imaging studies. Also, results supporting the performance of the imaging in these roles have largely been insufficient to justify their use within the design of a clinical trial or in treatment decision making. Statistically significant correlations between the imaging results and clinical outcomes are often incorrectly taken as evidence of adequate performance. Assessments of whether the imaging can outperform standard techniques or meaningfully supplement them are also frequently neglected. This paper provides guidance on study designs and statistical analyses for evaluating the performance of advanced imaging in the various roles in treatment decision guidance and clinical trial conduct. Relevant methodology from the imaging literature is reviewed; gaps in the literature are addressed using related concepts from the more extensive genomic and in vitro biomarker literature.
尽管人们普遍认为先进成像技术在指导肿瘤治疗决策以及提高治疗临床试验的效率和成功率方面会非常有帮助，但其被接受的速度却很缓慢。部分原因可能在于这些成像研究在研究设计和统计方法上存在差距。此外，支持成像技术在这些方面发挥作用的结果在很大程度上不足以证明其在临床试验设计或治疗决策中使用的合理性。成像结果与临床结果之间具有统计学意义的相关性常常被错误地视为性能良好的证据。对于成像技术是否能优于标准技术或有意义地补充标准技术的评估也常常被忽视。本文为评估先进成像技术在治疗决策指导和临床试验开展中的各种作用的性能提供了研究设计和统计分析方面的指导。对成像文献中的相关方法进行了综述；利用更广泛的基因组学和体外生物标志物文献中的相关概念来填补文献中的空白。
