Improving normal tissue complication probability models: the need to adopt a "data-pooling" culture.

Improving normal tissue complication probability models: the need to adopt a "data-pooling" culture.
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DOI:
10.1016/j.ijrobp.2009.06.094
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发表时间:
2010-03-01
影响因子:
7
通讯作者:
Marks, Lawrence B.
Marks, Lawrence B.
中科院分区:
医学1区
文献类型:
--
作者:
Deasy, Joseph O.;Bentzen, Soren M.;Jackson, Andrew;Ten Haken, Randall K.;Yorke, Ellen D.;Constine, Louis S.;Sharma, Ashish;Marks, Lawrence B.

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正如QUANTEC综述所证明的那样,正常组织反应对剂量-体积因子依赖性的临床研究经常令人困惑地不一致。加快进展的一个关键机会是开始在存储库中存储高质量的数据集。利用现有技术,可以方便地查询多个储存库,而不泄露受保护的健康信息,以确定相关的数据来源,供进一步分析。在获得机构批准后,数据可以汇集,大大提高了构建预测模型的能力,这些模型适用范围更广,更能准确识别关键预测因素(无论是剂量测定、基于图像的、临床的、社会经济的还是生物学的)。数据汇总已经在一些正常组织并发症概率研究中有效地进行,并且应该成为一种常见策略。
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.
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