Radiomics Applications in Head and Neck Tumor Imaging: A Narrative Review.
Mario TortoraLaura GeminiAlessandra ScaravilliLorenzo UggaAndrea PonsiglioneArnaldo StanzioneFelice D'ArcoGennaro D'AnnaRenato CuocoloPublished in: Cancers (2023)
Recent advances in machine learning and artificial intelligence technology have ensured automated evaluation of medical images. As a result, quantifiable diagnostic and prognostic biomarkers have been created. We discuss radiomics applications for the head and neck region in this paper. Molecular characterization, categorization, prognosis and therapy recommendation are given special consideration. In a narrative manner, we outline the fundamental technological principles, the overall idea and usual workflow of radiomic analysis and what seem to be the present and potential challenges in normal clinical practice. Clinical oncology intends for all of this to ensure informed decision support for personalized and useful cancer treatment. Head and neck cancers present a unique set of diagnostic and therapeutic challenges. These challenges are brought on by the complicated anatomy and heterogeneity of the area under investigation. Radiomics has the potential to address these barriers. Future research must be interdisciplinary and focus on the study of certain oncologic functions and outcomes, with external validation and multi-institutional cooperation in order to achieve this.
Keyphrases
- artificial intelligence
- machine learning
- deep learning
- big data
- lymph node metastasis
- clinical practice
- contrast enhanced
- convolutional neural network
- high resolution
- palliative care
- single cell
- human health
- type diabetes
- current status
- squamous cell carcinoma
- magnetic resonance imaging
- metabolic syndrome
- prostate cancer
- risk assessment
- computed tomography
- electronic health record
- stem cells
- robot assisted
- adipose tissue
- young adults
- skeletal muscle
- high throughput
- mass spectrometry
- weight loss