A paradigm shift in the prevention and diagnosis of oral squamous cell carcinoma.

Jan-Michaél HirschRonak SandyBengt HasséusJoakim Lindblad

Published in: Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology (2023)

This position paper highlights non-invasive methods for identifying patients with oral mucosal lesions at risk of malignant transformation. Reliable non-invasive methods for screening at-risk individuals bring the early diagnosis of OSCC within reach. The use of biomarkers to decide on a targeted therapy is most likely to improve the outcome. With the large-scale collection of samples following patients over time, combined with genomic analysis and modern machine-learning-based approaches for finding patterns in data, this path holds great promise.

Keyphrases

machine learning
big data
end stage renal disease
ejection fraction
newly diagnosed
peritoneal dialysis
prognostic factors
artificial intelligence
electronic health record
patient reported