Abstract
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.
Highlights
When planning a radiotherapy treatment, a compromise is made between coverage of the target and exposure of Organs At Risk (OAR)
The differences in the isodose lines for patients with grade 0–1 until grade 3 acute esophagus toxicity (AET) imply that for patients of increasing AET grade, their average esophagus surface that received high dose increases: All isodose lines expand along the length of the esophagus for patients with more complications
The proportion of Tmax,i that are larger than the observed value (Tmax = 3.81) gives an adjusted p-value of 0.02, i.e., there is a significant dose difference between the non-failure and the failure patients for this patient group at α = 0.05 significance level
Summary
When planning a radiotherapy treatment, a compromise is made between coverage of the target and exposure of Organs At Risk (OAR). While the dose to the designated target is generally uniform and homogeneous between patients, the dose to surrounding structures can be highly variable, depending on patient geometries, tumor locations, and treatment techniques. Such heterogeneous incidental dose distributions in patients might “accidentally” lead to different treatment outcomes regarding tumor control (e.g. if subclinical disease is important) or normal tissue toxicity. The purpose of introducing data mining in radiotherapy is to explore hypotheses for dose-response relationships. Variations in stem cells, tumor microscopic disease and radiosensitivity distributions can be expected to affect dose-response relationships. Data mining on incidental dose may yield suspicious anatomical features from which –based on biological or clinical
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