Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT Images

Abstract

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 a multi-center HNC cohort of 883 patients (524 patients for training, 359 for testing) provided within the context of the HECKTOR MICCAI challenge 2022. A bounding box of the extended oropharyngeal region was retrieved for each patient with fixed size of 224 $$\times $$ 224 $$\times $$ 224 mm $$^{3}$$ . Then the 3D nnU-Net architecture was adopted to carry out automatic segmentation of both primary tumor and lymph nodes. From the predicted segmentation mask, 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 a mean dice score of 0.7 for primary tumor and lymph nodes. For recurrence-free survival prediction, conventional and radiomics models obtained C-index values of 0.66 and 0.65 in the test set, respectively, while the combined model did not improve the prognostic performance (0.65).