1. Artificial Intelligence in Head and Neck Cancer Diagnosis: A Comprehensive Review with Emphasis on Radiomics, Histopathological, and Molecular Applications.
- Author
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Broggi, Giuseppe, Maniaci, Antonino, Lentini, Mario, Palicelli, Andrea, Zanelli, Magda, Zizzo, Maurizio, Koufopoulos, Nektarios, Salzano, Serena, Mazzucchelli, Manuel, and Caltabiano, Rosario
- Subjects
HEAD & neck cancer diagnosis ,DIAGNOSTIC imaging ,PREDICTION models ,ARTIFICIAL intelligence ,RADIOMICS ,HEAD & neck cancer ,DEEP learning ,MACHINE learning - Abstract
Simple Summary: This article highlights how artificial intelligence (AI) is revolutionizing the diagnosis and treatment of head and neck cancers (HNCs). This article explores the use of various AI techniques, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), in medical imaging, molecular profiling, and predictive modeling. AI has demonstrated improved accuracy in analyzing CT, MRI, and PET scans, often surpassing human radiologists. It is also applied to histopathology, automating the analysis of whole-slide images and assisting in tumor grading and segmentation. Additionally, AI aids in molecular analysis, particularly in mutation detection and treatment personalization. While AI shows promising results in enhancing diagnostic precision and workflow efficiency, challenges such as data standardization and model transparency still remain. The present review discusses the transformative role of AI in the diagnosis and management of head and neck cancers (HNCs). Methods: 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. Results: 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. Conclusions: 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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