ESTRO 2022 - Abstract Book

S279

Abstract book

ESTRO 2022

Conclusion We developed a new method for LSP contouring based on specialized MRN and MR/CT deformable registration technique. The preliminary dosimetric analysis has shown that LSP can receive doses exceeding historical and current recommendations if left unattended. This work will shed light on future dosimetric and clinical studies concerning LSP protection.

PD-0317 A novel method to predict OAR contour errors without a ground truth using geometric learning

E. Henderson 1 , A. Green 1 , M. van Herk 1 , E. Vasquez Osorio 1

1 The University of Manchester, Division of Cancer Sciences, Manchester, United Kingdom

Purpose or Objective The delineation of organs-at-risk (OARs) is a crucial step in radiotherapy planning. However, contouring is prone to variability even when observers follow detailed delineation guidelines or when contours are generated by an automated tool. We propose a novel method to automatically detect errors in OAR contours, without a ground truth, by predicting distances to a consensus guideline across the entire 3D surface of the OAR contour. Materials and Methods We trained a custom deep learning model which takes an OAR contour and planning CT as input, and outputs a 3D prediction map of distances to a consensus across the entire contour. Our model (fig 1.) is constructed from 3 parts: a small convolutional neural network (CNN) to extract information from the CT scan in patches surrounding the input contour; a graph neural network (GNN) to extract geometric information from the input contour and a multi-layer perceptron (MLP) to make distance predictions. To create a dataset with which to train our model, we start with 34 head and neck planning CTs with OAR contours of the parotid glands produced by an experienced oncologist and a radiographer 1 . The contours are flipped laterally resulting in 68 “general” parotid glands. The oncologist’s contours are perturbed 100 times by applying structured noise to yield a training dataset of 6800 perturbed OAR structures. We train the model to predict the nearest distances to the gold standard (the oncologist’s original contour) across the entire surface of the perturbed structures. We perform a five-fold cross-validation and evaluate the robustness of our method using the unseen contours of the radiographer. We compare our model’s predictions to the ground truth distances between the radiographer’s and oncologist’s contours, using a 2-sample Kolmogorov–Smirnov test to evaluate the similarity in the distribution of distances for each contour. We report the proportion of OAR contours for which our model’s predictions had no significant difference to the ground truth (p>0.1).

Results Our model’s predictions have no significant differences for 78% of the radiographer’s OAR contours for distances greater than the CT resolution (1.44mm) and smaller than the maximum perturbation generated in the training dataset (5mm). Figure 2a shows violin plots of the absolute distance distributions for each of the radiographer’s 68 contours. Figure 2b shows a 3D example of the oncologist’s and radiographer’s contours, the ground truth distances and our model’s prediction.