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

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.

Sex

All

Minimum age

18 Years

Maximum age

80 Years

Inclusion Criteria:

* Cancer patients confirmed by histology/cytology
* Patients with nasopharyngeal carcinoma in the training group are initially diagnosed
* Subjects voluntarily participate in the study

Exclusion Criteria:

* Subjects taking medication or diet may affect their tongue image (such as aluminum magnesium carbonate, traditional Chinese medicine rhubarb, etc.)
* The researchers determined that the subjects had other factors that could force them to terminate the study.

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