  Automatic segmentation of head and neck cancer (HNC) tumors and lymph nodes
plays a crucial role in the optimization treatment strategy and prognosis
analysis. This study aims to employ nnU-Net for automatic segmentation and
radiomics for recurrence-free survival (RFS) prediction using pretreatment
PET/CT images in multi-center HNC cohort. A multi-center HNC dataset with 883
patients (524 patients for training, 359 for testing) was provided in HECKTOR
2022. A bounding box of the extended oropharyngeal region was retrieved for
each patient with fixed size of 224 x 224 x 224 $mm^{3}$. Then 3D nnU-Net
architecture was adopted to automatic segmentation of primary tumor and lymph
nodes synchronously.Based on predicted segmentation, ten conventional features
and 346 standardized radiomics features were extracted for each patient. Three
prognostic models were constructed containing conventional and radiomics
features alone, and their combinations by multivariate CoxPH modelling. The
statistical harmonization method, ComBat, was explored towards reducing
multicenter variations. Dice score and C-index were used as evaluation metrics
for segmentation and prognosis task, respectively. For segmentation task, we
achieved mean dice score around 0.701 for primary tumor and lymph nodes by 3D
nnU-Net. For prognostic task, conventional and radiomics models obtained the
C-index of 0.658 and 0.645 in the test set, respectively, while the combined
model did not improve the prognostic performance with the C-index of 0.648.
