A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC) (NPC)

February 14, 2025 updated by: Pu-Yun OuYang, Sun Yat-sen University

Development and Validation of a Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma Using Histologic Whole Slide Images and Time-dependent Magnetic Resonance Images

(I) AI Model for Diagnosing Lymph Node Metastasis We developed an AI model to help diagnose whether a single lymph node in nasopharyngeal cancer has spread. The model uses MRI images of the lymph node and the area around it. It includes: 1.Automatically identifying the lymph nodes and the primary tumor. 2.Analyzing MRI images of the lymph node and surrounding area. 3.Using MRI scans before and after chemotherapy to track changes in the lymph node.

(II) AI Model for Predicting Lymph Node Metastasis We created an AI model that predicts whether a lymph node in a specific area has cancer. This model uses a combination of the primary tumor's pathology and MRI images of both the tumor and lymph node. It also tracks changes in the lymph node over time. The model includes: 1.Analyzing the tumor's pathology to identify specific lymphatic structures. 2.Using MRI scans to predict the likelihood of metastasis in a single lymph node. 3.Examining MRI scans before and after chemotherapy to help determine if the lymph node has metastasized.

(III) Verifying and Analyzing the Benefits of the AI Model We are testing the AI model to see how well it works and its potential benefits, including: 1.Checking if the AI can correct past diagnoses of recurrent lymph nodes in nasopharyngeal cancer, which could help guide treatment plans for radiotherapy. 2.Testing the model using biopsy results from head and neck cancer patients to see if it can accurately detect negative lymph nodes. 3.Running clinical trials to test the AI model's safety and effectiveness in guiding radiation treatment for upper neck and single lymph node areas in nasopharyngeal cancer. 4.Analyzing the economic benefits of using the AI model in radiation treatment for nasopharyngeal cancer.

Study Overview

Status

Recruiting

Conditions

Detailed Description

(I) AI Model for Assisting Diagnosis of Lymph Node Metastasis in Nasopharyngeal Carcinoma Based on MRI Features of the Lymph Node and Surrounding 3mm Area.

An AI model is developed to assist in diagnosing whether a single lymph node in nasopharyngeal carcinoma has metastasized, based on the MRI features of the lymph node itself and its surrounding 3mm area. Specifically, the model includes: 1.Automatic segmentation of the lymph node and primary lesion using a semi-supervised AI model. 2.Construction of a single dual-view AI model based on baseline MRI features of the lymph node itself and its surrounding 3mm area. 3.Development of a dual-time series dual-view AI model based on MRI features of the lymph nodes before and after induction chemotherapy.

(II) Multimodal AI Model for Predicting Lymph Node Metastasis. A multimodal AI model is constructed to predict metastasis of a single station lymph node and diagnose lymph node metastasis "from surface to point." This is based on pathological features of the nasopharyngeal primary lesion, MRI images, lymph node location, and other factors. Specifically, the model includes: 1.Construction of an AI model based on H&E stained digital pathology of the primary lesion to extract features of the tertiary lymphatic structure. 2.Development of a multimodal AI model integrating pathological features of the nasopharyngeal primary lesion and baseline MRI images of both the primary lesion and lymph node to predict the probability of lymph node metastasis in a single station. 3.Construction of a single or dual-time series multimodal AI model using the probability of lymph node metastasis and baseline MRI or two MRI scans before and after induction chemotherapy to diagnose whether a single lymph node in the station has metastasized.

(III) Verification and Economic Benefit Analysis of AI Models. The AI models are subjected to thorough verification and economic benefit analysis. Specifically, the process includes: 1.Retrospective correction of historical diagnoses of in situ recurrent lymph nodes in nasopharyngeal carcinoma patients using the AI model to verify its potential benefits in guiding prescription doses for single lymph node radiotherapy. 2.Validation of the AI model using pathological results from lymph node dissection in head and neck squamous cell carcinoma patients to assess the detection rate of clinically diagnosed negative lymph nodes. 3.Prospective clinical trials to evaluate the effectiveness and safety of the AI model in guiding prescription doses for upper neck radiotherapy and single lymph node radiotherapy in nasopharyngeal carcinoma patients. 4.Economic benefit analysis to illustrate the economic value of the AI model in guiding upper neck radiotherapy for nasopharyngeal carcinoma.

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

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

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China, 510060
        • Recruiting
        • Department of Radiation Oncology, Sun Yat-sen University Cancer Center
        • Contact:
        • Contact:
          • Pu-Yun OuYang

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with pathologically confirmed nasopharyngeal carcinoma

Description

Inclusion Criteria:

  1. The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III);
  2. MRI scan was performed at the initial diagnosis (before anti-tumor treatment), and transverse and coronal MRI images before treatment were available, including T1-weighted, T2-weighted and T1-enhanced scanning sequences.
  3. PET/CT scan was performed at the initial diagnosis (before anti-tumor treatment)
  4. When MRI and PET/CT were inconsistent in judging the benign or malignant nature of lymph nodes, the patient agreed to undergo cervical lymph node puncture and pathological examination.

Exclusion Criteria:

  1. The patient has undergone cervical lymph node radiotherapy for any reason
  2. Combined with other malignant tumors

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
Prospective Validation Cohort
Prospective patient enrollment to validate the diagnostic efficacy of the AI model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AUC
Time Frame: through study completion, an average of 2 year
AUC (Area Under the Curve) refers to the area under a performance curve, typically the ROC (Receiver Operating Characteristic) curve or PR (Precision-Recall) curve, that is used to evaluate the performance of a classification model. It is a single scalar value that provides an aggregate measure of a model's ability to distinguish between classes (e.g., positive and negative samples).
through study completion, an average of 2 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Sensitivity and Specificity
Time Frame: through study completion, an average of 2 year
Sensitivity and Specificity are fundamental metrics used to evaluate the performance of a classification model, especially in medical diagnostics, machine learning, and statistics. These metrics are used to measure how well a model can correctly identify positive and negative cases.
through study completion, an average of 2 year

Other Outcome Measures

Outcome Measure
Measure Description
Time Frame
Positive Predictive Value (PPV) and Negative Predictive Value (NPV)
Time Frame: through study completion, an average of 2 year
Positive Predictive Value (PPV) and Negative Predictive Value (NPV) are performance metrics used to evaluate the reliability of a model's predictions, particularly in binary classification tasks. These metrics focus on the accuracy of the model's positive and negative predictions.
through study completion, an average of 2 year

Collaborators and Investigators

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

Sponsor

Sun Yat-sen University

Collaborators

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
First People's Hospital of Foshan
Affiliated Cancer Hospital & Institute of Guangzhou Medical 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 (Actual)

November 19, 2024

Primary Completion (Estimated)

September 1, 2026

Study Completion (Estimated)

September 1, 2026

Study Registration Dates

First Submitted

February 8, 2025

First Submitted That Met QC Criteria

February 14, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

February 14, 2025

Last Verified

February 1, 2025

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • B2024-769-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

Individual participant data (IPD) might not be shared due to concerns about patient privacy, ethical considerations, or institutional policies. Restrictions may also arise from data protection regulations, confidentiality agreements, or the potential risk of re-identification. Additionally, if the data includes sensitive medical information, sharing may require special approvals or de-identification processes that are not feasible.

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

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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