Simplicity Is All You Need: Out-of-the-Box nnUNet Followed by Binary-Weighted Radiomic Model for Segmentation and Outcome Prediction in Head and Neck PET/CT

Automated lesion detection and segmentation might assist radiation therapy planning and contribute to the identification of prognostic image-based biomarkers towards personalized medicine. In this paper, we propose a pipeline to segment the primary and metastatic lymph nodes from fluorodeoxyglucose (FDG) positron emission tomography and computed tomography (PET/CT) head and neck (H &N) images and then predict recurrence free survival (RFS) based on the segmentation results. For segmentation, an out-of-the-box nnUNet-based deep learning method was trained and labelled the two lesion types as primary gross tumor volume (GTVp) and metastatic nodes (GTVn). For RFS prediction, 2421 radiomic features were extracted from the merged GTVp and GTVn using the pyradiomics package. The ability of each feature to predict RFS was measured using the C-index. Only the features with a C-index greater than \(C_{min}\), hyperparameter of the model, were selected and assigned a +1 or –1 weight as a function of how they varied with the recurrence time. The final RFS probability was calculated as the mean across all selected feature z-scores weighted by their +/–1 weight. The fully automated pipeline was applied to the data provided through the HECKTOR 2022 MICCAI challenge. On the test data, the fully automated segmentation model achieved 0.777 and 0.763 Dice scores on the primary tumor and lymph nodes respectively (0.770 on average). The binary-weighted radiomic model yielded a 0.682 C-index. These results allowed us to rank first for outcome prediction and fourth for segmentation in the challenge. We conclude that the proposed fully-automated pipeline from segmentation to outcome prediction using a binary-weighted radiomic model competes well with more complicated models. Team: LITO.