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

November 10, 2023 updated by: Fifth Affiliated Hospital, Sun Yat-Sen University

Development of a Machine Learning Model for Nasopharyngeal Carcinoma Screening Based on Tongue Imaging: a Prospective Multicenter Cross-sectional Study

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.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Estimated)

5000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Zhuhai, China
        • The Fifth Affiliated Hospital of Sun Yat sen University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

This study plans to include a training group consisting of 600 newly diagnosed nasopharyngeal carcinoma patients and 800 healthy individuals, as well as 800 individuals with common nasopharyngeal diseases and other tumors. According to the training group: validation group=6:4, configure the number of validation group members. There are approximately 5000 people in total.

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training group
Experimental group: population of initially diagnosed nasopharyngeal carcinoma [600 people]; Control group: 2400 healthy individuals+nasopharyngeal disease patients+other tumors.
Other: Tongue image
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.
Other: Tongue image
Using intelligent imaging devices to collect subject tongue images

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Area Under Curve (AUC) of Diagnostic Model
Time Frame: 12 months
Determine the screening effectiveness of the nasopharyngeal carcinoma tongue image model
12 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Fifth Affiliated Hospital, Sun Yat-Sen University

Investigators

  • Principal Investigator: Qi Zeng, Doctor, Fifth Affiliated Hospital, Sun Yat-Sen University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

November 15, 2023

Primary Completion (Estimated)

November 15, 2024

Study Completion (Estimated)

December 1, 2025

Study Registration Dates

First Submitted

November 2, 2023

First Submitted That Met QC Criteria

November 10, 2023

First Posted (Actual)

November 13, 2023

Study Record Updates

Last Update Posted (Actual)

November 13, 2023

Last Update Submitted That Met QC Criteria

November 10, 2023

Last Verified

November 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • ZDWY.ZYZLK.006

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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