Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Int J Radiat Oncol Biol Phys. 2010 Mar 1;76(3 Suppl):S151-4. doi: 10.1016/j.ijrobp.2009.06.094.
Clinical studies of the dependence of normal tissue response on dose-volume factors are often confusingly inconsistent, as the QUANTEC reviews demonstrate. A key opportunity to accelerate progress is to begin storing high-quality datasets in repositories. Using available technology, multiple repositories could be conveniently queried, without divulging protected health information, to identify relevant sources of data for further analysis. After obtaining institutional approvals, data could then be pooled, greatly enhancing the capability to construct predictive models that are more widely applicable and better powered to accurately identify key predictive factors (whether dosimetric, image-based, clinical, socioeconomic, or biological). Data pooling has already been carried out effectively in a few normal tissue complication probability studies and should become a common strategy.
临床研究表明，正常组织反应对剂量-体积因素的依赖性常常令人困惑地不一致。正如 QUANTEC 综述所表明的那样，加速进展的一个关键机会是开始在存储库中存储高质量数据集。使用现有技术，可以方便地查询多个存储库，而不会泄露受保护的健康信息，以确定进一步分析的相关数据源。在获得机构批准后，可以汇集数据，从而极大地提高构建更广泛适用且更有能力准确识别关键预测因素（无论是剂量学、基于图像、临床、社会经济还是生物学）的预测模型的能力。在一些正常组织并发症概率研究中已经有效地进行了数据汇集，并且应该成为一种常见的策略。
