Advanced Automatic Segmentation of Tumors and Survival Prediction in Head and Neck Cancer

Abstract

In this study, 325 subjects were extracted from the HECKTOR-Challenge. 224 subjects were considered in the training procedure, and 101 subjects were employed to validate our models. Positron emission tomography (PET) images were registered to computed tomography (CT) images, enhanced, and cropped. First, 10 fusion techniques were utilized to combine PET and CT information. We also utilized 3D-UNETR (UNET with Transformers) and 3D-UNET to automatically segment head and neck squamous cell carcinoma (HNSCC) tumors and then extracted 215 radiomics features from each region of interest via our Standardized Environment for Radiomics Analysis (SERA) radiomics package. Subsequently, we employed multiple hybrid machine learning systems) HMLS), including 13 dimensionality reduction algorithms and 15 feature selection algorithms linked with 8 survival prediction algorithms, optimized by 5-fold cross-validation, applied to PET only, CT only and 10 fused datasets. We also employed Ensemble Voting for the prediction task. Test dice scores and test c-indices were reported to compare models. For segmentation, the highest dice score of 0.680 was achieved by the Laplacian-pyramid fusion technique linked with 3D-UNET. The highest c-index of 0.680 was obtained via the Ensemble Voting technique for survival prediction. We demonstrated that employing fusion techniques followed by appropriate automatic segmentation technique results in a good performance. Moreover, utilizing the Ensemble Voting technique enabled us to achieve the highest performance.