Clinical Report: Novel Uses of Artificial Intelligence in Risk Assessment for Head and Neck Cancer
Overview
This scoping review identifies emerging applications of artificial intelligence (AI) in risk stratification for head and neck cancer, highlighting its potential to enhance diagnostic and prognostic accuracy. The findings underscore the need for methodological standardization and validation to ensure effective clinical implementation.
Background
Head and neck cancer poses significant challenges due to its biological and anatomical diversity, which complicates accurate risk assessment using conventional staging systems. The integration of AI into clinical practice could optimize diagnostic and therapeutic decision-making, ultimately leading to more personalized treatment approaches. Understanding the role of AI in this context is crucial for improving patient outcomes.
Data Highlights
A total of 44 studies were included, focusing on AI applications for diagnostic tasks and prognostic risk stratification in head and neck cancer.
Key Findings
AI techniques were primarily applied to predict lymph node metastasis and extranodal extension.
Machine learning models, deep learning architectures, and radiomics-based methods were the most frequently employed approaches.
Common data modalities included computed tomography, magnetic resonance imaging, and digital histopathology.
Studies reported moderate to high predictive performance, but exhibited substantial methodological heterogeneity.
There was a predominance of retrospective designs and limited external validation of AI models.
Clinical Implications
Clinicians should consider the integration of AI tools to enhance risk stratification and improve decision-making in head and neck cancer management. However, careful attention must be paid to the methodological rigor and validation of these AI models before widespread clinical adoption.
Conclusion
AI has the potential to significantly improve risk stratification in head and neck cancer, but challenges related to validation and implementation must be addressed to realize its full benefits in clinical practice.
