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

NCT06829147 | Recruiting

• 1 other identifier: B2024-769-01
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

(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.

Conditions

Nasopharyngeal Cancinoma (NPC); Lymph Node Metastasis

Keywords

Nasopharyngeal carcinoma; Cervical lymph node metastasis; MRI; pathology; Artificial Intelligence Diagnostic Model

Outcome Measures

Primary Outcomes (1)

• AUC

  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 Outcomes (1)

• Sensitivity and Specificity

  through study completion, an average of 2 year

Other Outcomes (1)

• Positive Predictive Value (PPV) and Negative Predictive Value (NPV)

  through study completion, an average of 2 year

Study Arms (1)

Prospective Validation Cohort

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

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with pathologically confirmed nasopharyngeal carcinoma

You may qualify if:

• The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III);
• 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.
• PET/CT scan was performed at the initial diagnosis (before anti-tumor treatment)
• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Sun Yat-sen University: lead
• First People's Hospital of Foshan: collaborator
• Affiliated Cancer Hospital & Institute of Guangzhou Medical University: collaborator
• Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University: collaborator

Study Sites (1)

Department of Radiation Oncology, Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, 510060, China

RECRUITING

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MeSH Terms

Conditions

Lymphatic Metastasis; Nasopharyngeal Carcinoma

Condition Hierarchy (Ancestors)

Neoplasm Metastasis; Neoplastic Processes; Neoplasms; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Nasopharyngeal Neoplasms; Pharyngeal Neoplasms; Otorhinolaryngologic Neoplasms; Head and Neck Neoplasms; Neoplasms by Site; Nasopharyngeal Diseases; Pharyngeal Diseases; Stomatognathic Diseases; Otorhinolaryngologic Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Chief Physician

Study Record Dates

• First Submitted: February 8, 2025
• First Posted: February 17, 2025
• Study Start: November 19, 2024
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: February 17, 2025

Record last verified: 2025-02

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)