Development of a Machine Learning Model for Nasopharyngeal Carcinoma Screening Based on Tongue Imaging

Area Under Curve (AUC) of Diagnostic Model

Nasopharyngeal cancer is common in China, Southeast Asia, and North Africa, and is usually
 associated with Epstein-Barr virus (EBV) infection. Using EBV specific antibodies or EBV DNA
 screening can increase the proportion of patients diagnosed with early nasopharyngeal
 carcinoma from approximately 20% to over 70%. However, the application of nasopharyngeal
 carcinoma screening in clinical practice is hindered by low positive predictive values, even
 in areas where the EB virus is prevalent in China, the positive predictive value is only
 4.8%. Therefore, there is an urgent need to identify new biomarkers or strategies with high
 sensitivity and positive predictive value for nasopharyngeal carcinoma screening.
 
 A study published in the Lancet sub journal 《eClinicalMedicine》 in 2023 showed that a tongue
 image model based on machine learning can serve as a stable diagnostic method for gastric
 cancer (AUC=0.89), and has been clinically validated in multiple centers. This study inspires
 researchers to introduce artificial intelligence machine learning technology into the
 diagnosis and treatment of nasopharyngeal cancer.
 
 In summary, this plan explores the establishment of tongue image machine learning models in
 nasopharyngeal carcinoma patients to help improve the positive predictive value of
 nasopharyngeal carcinoma screening.