ESTRO 2022 - Abstract Book

S1575

Abstract book

ESTRO 2022

using either 3D conformal RT or intensity-modulated RT. Recurrence was defined as the confirmed appearance of new contrast uptake at recurrence time compared to baseline. A simple task learning strategy based on a modified 3D U-Net architecture was implemented first, with the goal to identify recurrence areas based on the 3 baseline MRI. Sequential and parallel multitask models aiming at segmenting the enhancing part of the tumor at T Baseline in addition to recurrence areas were considered also. Dice Loss, Binary Cross-Entropy (BCE) Loss, and Dice/BCE Loss were used for optimization. The dataset was randomly split into training (159 patients) and testing (40 patients) sets. Models were trained from scratch using a five-fold cross-validation procedure. Ensembling of the models obtained for the five folds was used for inference on the test set. Networks’ performances were evaluated using the Dice coefficient (DSC) as well as balanced accuracy, sensitivity, and specificity metrics. Results Among the 199 patients evaluated, 140 had local recurrence (70%), and 175 (88%) had at least part of the recurring tumor developing from the edema. Table 1 summarizes the results obtained on the test set. The best model obtained a DSC of 0.201. Models exploiting multitasking did not improve performance. The use of a sum of the two losses (Dice + BCE) did not show either a significant contribution whatever the model.