Project ID: P202102100002
Prediction of TNM Stage in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics Features
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: The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head-and-neck-squamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist with assessment of disease status, prognosis, and management. This study aims to predict TNM staging for HNSCC cancer via Hybrid-Machine-Learning-Systems (HMLSs) and radiomics-features. Methods: In our study, 408 patients were included from the Cancer-Imaging-Archive (TCIA) database, derived in a multi-center setting. PET images were registered to CT, enhanced, and cropped. 215 radi-omics-features were extracted from each region-of-interest via our standardized SERA radiomics-package. We employed multiple HMLSs including 16 feature-extraction (FEA)+9 feature-selection-algorithms (FSA) linked with 8 classifiers optimized by grid-search approach, with model training, fine-tuning and selection (5-fold-cross-validation; 319 patients), followed by external-testing (89 patients), for 9 datasets including CT, PET, and 7 image-fused-datasets. Datasets were normalized by Z-score-technique, with ac-curacy reported to compare models.