A Deep Learning Model for Diagnosing Lymph Node Metastasis in Nasopharyngeal Carcinoma(NPC)
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorSun Yat-sen University
About this trial
(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
This trial does not accept healthy volunteersQualifiers
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.
Disqualifiers
The patient has undergone cervical lymph node radiotherapy for any reason
Combined with other malignant tumors
Trial population
Patients with pathologically confirmed nasopharyngeal carcinoma
Trial design
Design model
Cohort
Time perspective
Prospective
Treatments tested in this trial
Not listed
Trial groups
500 Participants
are grouped into 1 trial groupGroup A: Prospective Validation Cohort
Trial outcomes
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).
Time frame
through study completion, an average of 2 yearSecondary outcomes
1
Sensitivity and Specificity
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.
Time frame
through study completion, an average of 2 yearOther outcomes
1
Positive Predictive Value (PPV) and Negative Predictive Value (NPV)
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.
Time frame
through study completion, an average of 2 yearSponsors and contacts
Click on the lead sponsor to view all of their trials.
Sun Yat-sen University
Lead sponsor
Sun Yat-Sen University Cancer Center
Sponsor institution
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
Source ClinicalTrials.gov