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

NCT ID: NCT06129201

Last Updated: 2023-11-13

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 5000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-11-15
• Study Completion Date: 2025-12-01

Brief Summary

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.

Detailed Description

Nasopharyngeal cancer is common in China, Southeast Asia, and North Africa, and is generally 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%. In previous studies, researchers found that participants who underwent screening were more likely to achieve long-term survival after being diagnosed with nasopharyngeal carcinoma compared to the control group, and the risk of nasopharyngeal carcinoma specific death was lower among screened patients (relative risk 0.22). 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%. More than 95% of high-risk participants identified through primary serological screening underwent unnecessary and time-consuming clinical examinations and follow-up. The combination of various biomarkers, multi-step screening, and identification of new biomarkers are used to improve the performance of nasopharyngeal cancer screening strategies. However, the progress achieved so far is still unsatisfactory, characterized by low sensitivity, complex operation, or high cost. Therefore, there is an urgent need to identify new biomarkers or strategies with high sensitivity and positive predictive value for nasopharyngeal carcinoma screening.

In 《The New England Journal of Medicine》 in 2023, Professor Xia Ningshao's team reported on the identification and validation of anti BNLF2 total antibody (P85Ab) as a new serological biomarker for nasopharyngeal cancer screening.The sensitivity of P85-Ab nasopharyngeal carcinoma is 97.9%, with a positive predictive value of 10.0%. Furthermore, on the basis of P85-Ab positivity, if further detection of EB double antibodies (EBV nuclear antigen 1 [EBNA1]-IgA and EBV-specific viral capsid antigen [VCA]-IgA) is carried out, intermediate or medium high risk individuals with EB double antibodies can undergo nasopharyngoscopy examination, which can increase the positive predictive value of nasopharyngeal carcinoma screening to 29.6% -44.6%, that is, for every 2-3 nasopharyngoscopes performed, one case of nasopharyngeal carcinoma can be diagnosed. The sensitivity of this study is very high, but the positive predictive value is only 10%. Even when combined with traditional EB dual antibody monitoring and nasal endoscopy, one-third to one-half of non nasopharyngeal carcinoma patients still undergo unnecessary and time-consuming clinical examinations. Therefore, it is still necessary to explore simple and cost-effective methods to improve the strategy of 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.

Conditions

Nasopharyngeal Carcinoma

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: CROSS_SECTIONAL

Study Groups

Training group

Experimental group: population of initially diagnosed nasopharyngeal carcinoma \[600 people\]; Control group: 2400 healthy individuals+nasopharyngeal disease patients+other tumors.

Tongue image

Intervention Type: OTHER

Using intelligent imaging devices to collect subject tongue images

Validation group

Validation group: Experimental group: Nasopharyngeal cancer population \[400 people\]; Control group: 1600 healthy individuals+patients with nasopharyngeal diseases+other tumors.

Tongue image

Intervention Type: OTHER

Using intelligent imaging devices to collect subject tongue images

Interventions

Tongue image

Using intelligent imaging devices to collect subject tongue images

Intervention Type: OTHER

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Fifth Affiliated Hospital, Sun Yat-Sen University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Qi Zeng, Doctor

Role: PRINCIPAL_INVESTIGATOR

Fifth Affiliated Hospital, Sun Yat-Sen University

Locations

The Fifth Affiliated Hospital of Sun Yat sen University

Zhuhai, , China

Countries

China

Central Contacts

Yulong Zhang, Doctor

Role: CONTACT

zhongxiyi1101@163.com

18810550602

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Other Identifiers

ZDWY.ZYZLK.006

Identifier Type: -

Identifier Source: org_study_id