Abstract Book

S1094

ESTRO 37

thereby controlling for tumour size, and age (Figure 1). Voxels in which the hazard ratio was statistically significant were labelled using a permutation test method.

Figure 2 risk group identification based on Rad_index (Low risk: Rad_index ＜ -0.00903; High risk: Rad_index≥- 0.00903). Conclusion Compactness, Wavlet_GLCM_Std, and Wavlet_GLRMS_RLN might have strong association with prognostics of inoperable NSCLC patients with ALK+ mutation, of which the radiomics signature was comprised, demonstrating well-validated value for predicting and identifying high or low risk patients in non-targeted treatment group. These finding indicate that the radiomics signature should be used with cautions in ALK+ mutated patients, and is prospectively applicable as prognostic indicator for inoperable ALK+ mutated patients received non-targeted treatment. However, in terms of patients received targeted therapy, further investigation is needed. EP-2008 Image based data mining using per-voxel Cox regression A. Green 1 , A. McWilliam 1 , E. Vasquez-Osorio 1 , W. Beasley 1 , M. Van Herk 1 1 Manchester Cancer Research Centre- Division of Cancer Science- School of Medical Sciences- Faculty of Biology- Medicine and Health- University of Manchester- Manchester Academic Health Sciences Centre- Manchester- UK- M13 9PL- UK, Radiotherapy Related, Purpose or Objective Recent work has shown the efficacy of image based data mining (IBDM) relating planned radiotherapy dose to treatment outcome for hypothesis generation in dose- effect modelling. However, all techniques used to date do not account for confounding variables in the image based statistic used to identify dose sensitive regions, instead relying on correction after identifying a region of interest. In this work, we introduce a novel IBDM technique in which a Cox regression is performed in each voxel of the 4D dose distribution obtained by mapping all patients to a reference patient. By using a Cox regression in this way, it is possible to control for other variables when assessing the impact of radiation dose on tissue on a per-voxel basis providing unbiased estimates of the location of dose-response, which is particularly important for variables that affect dose and outcome such as GTV volume. Material and Methods A cohort of 870 lung cancer patients was collected, including planning CT, planned dose distribution, outcome and clinical data. Dose distributions were spatially normalised using deformable image registration to a common frame of reference. A mask was produced such that only regions receiving a mean dose greater than 10 Gy were included in the analysis to remove low-dose regions which would lead to spurious Cox-regression fits. Hazard ratio for dose per Gy and the other variables was calculated in each voxel of the reference anatomy,

Results A statistically significant region, in which excess dose is associated with poor survival, was identified in the base of the heart (Figure 2). This region is similar to previous findings using IBDM on a similar cohort but has shifted slightly inferior compared to previous analyses. The hazard ratio in this region is between 1.012 and 1.017 Gy - 1 .

Conclusion IBDM using a Cox regression per voxel is feasible and identified a region in the heart in which excess dose is related to poorer survival, in agreement with recent literature. This technique is more selective than standard IBDM techniques, in which confounding variables are accounted for after the identification of a region and is therefore well suited for the presented heart case where increasing tumour size increases dose to the region of interest while being associated with a reduction of survival. This technique may lead to more easily testable hypotheses and accelerate clinical benefit. Further work will include additional variables, such as tumour stage and performance status and explore interaction of variables.