Project ID: P202102100001
Advanced Survival Prediction in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features
Survival prediction algorithms, deep learning, machine learning, fusion methods, image processing
People with the below expertise are able to apply for this project: 1-The individual with enough experience in traditional and deep learning fusion techniques. 2-The individual with enough experience in extracting radiomics features from each region of interest (via SERA package). 3-The individual with enough experience in machine learning algorithms including dimension reduction algorithms, classifiers. Both programming languages Matlab and Python are acceptable, but Python works better for some people who aim at working on google Colab.
Objective: Accurate prognostic stratification of Head-and-Neck-Squamous-Cell-Carcinoma (HNSCC) patients can be an important clinical reference when designing therapeutic strategies. We set to predict 4 outcomes: overall-survival (OS), distant-metastasis (DM), loco-regional-recurrence (LR), and progression-free-survival (SP). We studied hybrid-machine-learning-systems (HMLS) incorporating multiple dimensionality reduction as well as survival prediction algorithms, applied to datasets with radiomics-features. Methods: In this multi-center-study, 408 HNSCC patients were extracted from the Cancer-Imaging-Archive. PET images were registered to CT, enhanced, and cropped. 215 radiomics features were extracted from each region-of-interest via our standardized SERA radiomics package. We employed multiple HMLSs: 14 feature-extraction (FEA) or 10 feature-selection-algorithms (FSA) linked with 8 survival-prediction-algorithms (SPA) optimized by 5-fold cross-validation, applied to PET-only, CT-only and 4 PET-CT datasets generated by image-level fusion strategies. Datasets were normalized by Z-score-technique, with c-index reported to compare models.