Multiple comparisons permutation test for image based data mining in radiotherapy.
Multiple comparisons permutation test for image based data mining in radiotherapy.
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DOI：
10.1186/1748-717x-8-293
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发表时间：
2013-12-23
期刊：
影响因子：
--
通讯作者：
van Herk M
中科院分区：
文献类型：
--
作者：
Chen C;Witte M;Heemsbergen W;van Herk M
比较具有不同结果的患者的偶然剂量分布（即图像）是探索放射治疗中剂量反应假设的直接方法。在本文中，我们引入了一种排列测试，可以比较图像（例如放射治疗的剂量分布），同时解决多重比较问题。提出了检验统计量 Tmax，将图像之间的差异总结为单个值，并采用排列程序来计算调整后的 p 值。我们在两项回顾性研究中演示了该方法：一项将 3D 剂量分布与失败相关联的前列腺研究，以及将食管的 2D 表面剂量分布与急性食管毒性相关联的食道研究。结果，我们能够识别与失败（前列腺研究）或毒性（食道研究）显着相关的可疑区域。排列测试可以直接比较不同患者类别的图像，是放射治疗数据挖掘的有用工具。
Comparing incidental dose distributions (i.e. images) of patients with different outcomes is a straightforward way to explore dose-response hypotheses in radiotherapy. In this paper, we introduced a permutation test that compares images, such as dose distributions from radiotherapy, while tackling the multiple comparisons problem. A test statistic Tmax was proposed that summarizes the differences between the images into a single value and a permutation procedure was employed to compute the adjusted p-value. We demonstrated the method in two retrospective studies: a prostate study that relates 3D dose distributions to failure, and an esophagus study that relates 2D surface dose distributions of the esophagus to acute esophagus toxicity. As a result, we were able to identify suspicious regions that are significantly associated with failure (prostate study) or toxicity (esophagus study). Permutation testing allows direct comparison of images from different patient categories and is a useful tool for data mining in radiotherapy.