Description:

Multi-level multi-modality fusion radiomics is a promising technique with potential for improved prognostication and segmentation of cancer. This study aims to employ advanced fusion techniques, deep learning segmentation methods, and survival analysis to automatically segment tumor and predict survival outcome in head-and-neck-squamous-cell-carcinoma (HNSCC) cancer. 325 patients with HNSCC cancer were extracted from HECTOR Challenge. 224 patients were used for training and 101 patients were employed to finally validate models. 5 fusion techniques were utilized to combine PET and CT information. The rigid registration technique was employed to register PET images to their CT image. We employed 3D-UNet architecture and SegResNet (segmentation using autoencoder regularization) to improve segmentation performance. Radiomics features were extracted from each region of interest (ROI) via the standardized SERA package, applying to Hybrid Machine Learning Systems (HMLS) including 7 dimensionality reduction algorithms followed by 5 survival prediction algorithms. Dice score and c-Index were reported to compare models in segmentation and prediction tasks respectively. For segmentation task, we achieved dice score around 0.63 using LP-SR Mixture fusion technique (the mixture of Laplacian Pyramid (LP) and Sparse Representation (SR) fusion techniques) followed by 3D-UNET. Next that, employing LP-SR Mixture linked with GlmBoost (Gradient Boosting with Component-wise Linear Models) technique enables an improvement of c-Index ~0.66. This effort indicates that employing appropriate fusion techniques and deep learning techniques results in the highest performance in segmentation task. In addition, the usage of fusion techniques effectively improves survival prediction performance.