  For Head and Neck Cancers (HNC) patient management, automatic gross tumor
volume (GTV) segmentation and accurate pre-treatment cancer recurrence
prediction are of great importance to assist physicians in designing
personalized management plans, which have the potential to improve the
treatment outcome and quality of life for HNC patients. In this paper, we
developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation
method based on combined pre-treatment positron emission tomography/computed
tomography (PET/CT) scans of HNC patients. We extracted radiomics features from
the segmented tumor volume and constructed a multi-modality tumor
recurrence-free survival (RFS) prediction model, which fused the prediction
results from separate CT radiomics, PET radiomics, and clinical models. We
performed 5-fold cross-validation to train and evaluate our methods on the
MICCAI 2022 HEad and neCK TumOR segmentation and outcome prediction challenge
(HECKTOR) dataset. The ensemble prediction results on the testing cohort
achieved Dice scores of 0.77 and 0.73 for GTVp and GTVn segmentation,
respectively, and a C-index value of 0.67 for RFS prediction. The code is
publicly available (https://github.com/wangkaiwan/HECKTOR-2022-AIRT). Our
team's name is AIRT.
