ESTRO 37 Abstract book

S1091

ESTRO 37

Purpose or Objective Image-based data mining (IBDM) is a hypothesis- generating technique used to locate anatomy with dose- response relationships from previously treated radiotherapy patients without requiring prior region of interest delineation. Using deformable image registra- tion, dose distributions are spatially normalised into a common frame of reference, and the dose at each voxel is related to a clinical outcome to locate organs or substructures showing a dose-response relationship. Previous IBDM applications used binary outcome variables (e.g. yes/no recurrence), but several clinical endpoints are continuous variables (e.g. amount of trismus after radiotherapy). We introduce here IBDM for continuous outcomes. Using simulated data, we compare this technique with traditional binary IBDM, for which continuous data are dichotomised with a threshold. Material and Methods The simulated dose images consisted of a set of 1000 128x128 images with a uniform background pixel value of 0.5 and a central 20x20 region in which the pixel value was set to increase from 0 to 1 across the set of images (Figure 1a). Gaussian noise was added to the dose images and each image was blurred with a Gaussian kernel, simulating incidental radiotherapy dose (Figure 1b). Each image in the set had a corresponding outcome label, which was a number that increased linearly between 0 and 100 across the image set. Gaussian noise was added to the outcome data to simulate a correlation <1 in the central region. For the simulation, 30 image-outcome pairs (i.e. a dose image and its corresponding outcome value) were randomly selected from the set of 1000 images. Binary IBDM using the t-statistic was performed using a range of thresholds for dichotomisation. Continuous IBDM used the Spearman correlation between each pixel value and the continuous outcome. In both techniques, permutation testing classified pixels with a statistically significant dose-response relationship. The classification of each pixel was compared to the true labelling and the sensitivity was calculated for each technique. This was repeated 100 times, calculating the average pixel classification, sensitivity and overall significance.

Results Continuous IBDM successfully identified the central region, whereas binary IBDM was only successful for thresholds close to the centre of the outcome distribution (Figure 2a). Continuous IBDM had a higher sensitivity and a lower statistical significance than binary IBDM for any threshold (Figure 2b).

Conclusion Continuous IBDM was more sensitive to dose-response relationships than binary IBDM with a dichotomised endpoint, with binary IBDM strongly influenced by the choice of threshold. Continuous IBDM allows anatomy with significant dose-response relationships to be spatially localised, and improved understanding of these dose-response relationships will help optimise radiotherapy. We are currently applying this technique to study trismus in head and neck cancer patients. EP-2001 A Bayesian network model for personalized elective CTV definition in head & neck cancer B. Pouymayou 1 , O. Riesterer 1 , M. Guckenberger 1 , J. Unkelbach 1 1 University Hospital Zurich and University of Zurich, Department of Radiation Oncology, Zürich, Switzerland Purpose or Objective Definition of the clinical target volume (CTV) is one of the largest sources of uncertainties in radiotherapy. In the case of head & neck cancer, the CTV contains lymph node (LN) levels that are at risk of harboring microscopic metastases despite negative findings on imaging. Thereby, a large portion of the neck is irradiated prophylactically, adding to treatment-related toxicity. Currently, population-based guidelines are being used to determine the LN levels to be included in the CTV, which typically only incorporate the site of the primary tumor (PT) (e.g. oropharynx, hypopharynx or larynx) and N-