Original Title: Artificial Intelligence in cancer risk assessment of oral potentially malignant disorders: applications and challenges
Journal: International journal of surgery (London, England)
DOI: 10.1097/JS9.0000000000003363
Overview
This article examines the role of artificial intelligence in evaluating the risk of malignant transformation in oral potentially malignant disorders. Traditionally, clinicians rely on oral epithelial dysplasia grading to determine cancer risk. However, this method is often limited by human subjectivity and an inability to incorporate various risk factors simultaneously. Artificial intelligence offers a method to integrate diverse datasets, including demographic information, smoking history, clinical images, and histopathology slides. By analyzing both structured and unstructured data, these computational models can provide an objective assessment of the probability that a lesion will progress to cancer. The text outlines the necessary steps for clinical implementation, ranging from data preprocessing to full-scale deployment, while highlighting current challenges related to data quality and ethical considerations.
Novelty
The novelty of this work lies in its systematic categorization of the clinical implementation pathway for artificial intelligence in oral oncology. It emphasizes the importance of data preprocessing techniques, such as oversampling and data augmentation, to address the frequent issue of class imbalance in medical datasets. Furthermore, the article identifies a significant gap in current research, noting that existing models predominantly focus on oral leukoplakia. It argues for the development of subtype-specific models for high-risk conditions like proliferative verrucous leukoplakia and erythroplakia, which exhibit higher rates of malignant transformation. By advocating for multimodal systems that combine clinical, imaging, and molecular markers like p53 or loss of heterozygosity, the work provides a framework for moving beyond single-modality experimental models toward integrated precision medicine tools.
Potential Clinical / Research Applications
The primary clinical application of these findings is the refinement of risk stratification for patients with oral lesions. By providing a personalized malignant transformation probability, artificial intelligence can assist surgeons in deciding between conservative monitoring and aggressive surgical intervention. In a research context, the integration of molecular biomarkers such as p53 expression into computational models can help identify the underlying biological drivers of progression. Furthermore, the development of standardized data collection protocols across multiple centers will facilitate the creation of larger, more diverse datasets. This will enable the validation of subtype-specific models, ensuring that rare but high-risk disorders are not overlooked. Ultimately, these applications aim to reduce human error in diagnosis and improve the long-term survival rates of patients by enabling earlier detection of oral cancer.
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