Abstract
The ability to accurately diagnose and analyze head and neck (H&N) tumors in head and neck cancer (HNC) is critical in the administration of patient specific radiation therapy treatment and predicting patient survivability outcome using radiomics. An automated segmentation method for H&N tumors would greatly assist in optimizing personalized patient treatment plans and allow for accurate feature extraction, via radiomics or other means, to predict patient prognosis. In this work, a three-dimensional UNET network was trained to segment H&N primary tumors using a framework based on nnUNET. Multimodal positron emission tomography (PET) and computed tomography (CT) data from 224 subjects were used for model training. Survival forest models were applied to patient clinical data features in conjunction with features extracted from the segmentation maps to predict risk scores for time to progression events for every patient. The selected segmentation methods demonstrated excellent performance with an average DSC score of 0.78 and 95% Hausdorff distance of 3.14. The random forest model achieved a C-index of 0.66 for predicting the Progression Free Survival (PFS) endpoint.
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Murugesan, G.K. et al. (2022). Head and Neck Primary Tumor Segmentation Using Deep Neural Networks and Adaptive Ensembling. In: Andrearczyk, V., Oreiller, V., Hatt, M., Depeursinge, A. (eds) Head and Neck Tumor Segmentation and Outcome Prediction. HECKTOR 2021. Lecture Notes in Computer Science, vol 13209. Springer, Cham. https://doi.org/10.1007/978-3-030-98253-9_21
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