Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications

The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs). It explores how AI technologies, including ML, DL, and CNNs, are applied in various diagnostic tasks, such as medical imaging, molecular profiling, and predictive modeling. Thi...

Full description

Saved in:
Bibliographic Details
Published in:Cancers 2024-10, Vol.16 (21), p.3623
Main Authors: Broggi, Giuseppe, Maniaci, Antonino, Lentini, Mario, Palicelli, Andrea, Zanelli, Magda, Zizzo, Maurizio, Koufopoulos, Nektarios, Salzano, Serena, Mazzucchelli, Manuel, Caltabiano, Rosario
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Cancer
Cancer therapies
Care and treatment
Chemotherapy
Clinical outcomes
CT imaging
Deep learning
Development and progression
Diagnosis
Head & neck cancer
Head and neck
Image processing
Lymphatic system
Machine learning
Medical imaging
Medical imaging equipment
Medical prognosis
Neural networks
Patients
PET imaging
Radiation
Radiomics
Review
Technology application
Traumatic brain injury
Tumors
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs). It explores how AI technologies, including ML, DL, and CNNs, are applied in various diagnostic tasks, such as medical imaging, molecular profiling, and predictive modeling. This review highlights AI's ability to improve diagnostic accuracy and efficiency, particularly in analyzing medical images like CT, MRI, and PET scans, where AI sometimes outperforms human radiologists. This paper also emphasizes AI's application in histopathology, where algorithms assist in whole-slide image (WSI) analysis, tumor-infiltrating lymphocytes (TILs) quantification, and tumor segmentation. AI shows promise in identifying subtle or rare histopathological patterns and enhancing the precision of tumor grading and treatment planning. Furthermore, the integration of AI with molecular and genomic data aids in mutation analysis, prognosis, and personalized treatment strategies. Despite these advancements, the review identifies challenges in AI adoption, such as data standardization and model interpretability, and calls for further research to fully integrate AI into clinical practice for improved patient outcomes.
ISSN:2072-6694
2072-6694
DOI:10.3390/cancers16213623