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
Trial ID: NCT06829147
Status: RECRUITING
Study type: OBSERVATIONAL
Start date: 2024-11-19
Completion date: 2026-09-01
Sponsor: Sun Yat-sen University
Collaborators: First People's Hospital of Foshan; Affiliated Cancer Hospital & Institute of Guangzhou Medical University; Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Conditions: Nasopharyngeal Cancinoma (NPC); Lymph Node Metastasis
Locations: Department of Radiation Oncology, Sun Yat-sen University Cancer Center
Countries: China
Enrollment: 500
Age range: None - None
Sex: ALL

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.

Eligibility criteria:
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

Outcomes:
AUC