Head and neck squamous cell carcinoma (HNSCC) is the seventh most prevalent cancer worldwide, and occur in a wide range of primary sites in the head and neck region. The need for more personalized care is particularly evident in head and neck cancer (HNC), which exhibits significant heterogeneity in clinical presentation, tumor biology, and outcomes (6, 7), making it difficult to select the optimal management strategy for each patient. Multi-model treatments are used as the first-line treatment for HNSCC patients, including surgery, radiotherapy, chemotherapy, and immunotherapy, depending on the particular tumor position and staging. A biopsy is crucial for the histopathological examination of suspicious lesions. Imaging studies such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) scans are used to assess the extent of disease and aid in staging.
Recent technological advancements in computer science, availability of large medical imaging datasets, and improved ML/DL algorithms have further enhanced the potential for application of AI in oncology. As a result, several promising studies emphasizing the diagnostic and prognostic potentials of AI models as an assistant decision-making tool have been reported during the last decade. The application of ML in the analysis of MRI, CT and PET-CT in head and neck tumors presents exciting opportunities for improving diagnosis, staging treatment planning and patients outcome. Continued research and development in this area will likely lead to more sophisticated tools that integrate artificial intelligence into routine practice.
The scope of this topic is to explore the potential and applications of artificial intelligence in the diagnosis, staging, and outcome prediction of head and neck tumors within the context of personalized medicine. Relevant areas include, but are not limited to, image segmentation, radiomics, classification, and the integration of multi-modal imaging, with an emphasis on advancing comprehensive and innovative AI-driven approaches in this rapidly evolving field.
Keywords: MRI; CT; PET-CT- Machine Learning - Head and neck tumors
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