Actively Recruiting
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
Led by Sun Yat-sen University · Updated on 2025-02-25
500
Participants Needed
2
Research Sites
97 weeks
Total Duration
On this page
Sponsors
S
Sun Yat-sen University
Lead Sponsor
F
First Affiliated Hospital, Sun Yat-Sen University
Collaborating Sponsor
AI-Summary
What this Trial Is About
An AI model was developed to predict the likelihood of distant metastasis in patients with nasopharyngeal cancer based on pathology slides and MRI scans of the primary tumor. The model was validated using data from multiple centers. It was then applied to patients with advanced stages who were recommended to undergo PET/CT scans based on the NCCN or CSCO guidelines. This AI model can accurately screen patients with high risk of distant metastasis at the time of initial diagnosis to receive PET/CT, avoid excessive examination of patients with low risk of distant metastasis, save medical resources and reduce the economic burden on patients.
CONDITIONS
Official Title
Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI
Who Can Participate
Eligibility Criteria
You may qualify if you...
- Pathologically confirmed nasopharyngeal carcinoma (WHO types I, II, or III)
- Tumor stage T3-4 or lymph node stage N2-3
- MRI scans of the nasopharynx and neck performed, including plain and enhanced scans
- Underwent PET/CT or conventional imaging to screen for distant metastases
You will not qualify if you...
- History of other malignant tumors such as head and neck squamous cell carcinoma, thyroid cancer, breast cancer, esophageal cancer, etc.
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
Trial Site Locations
Total: 2 locations
1
Department of Radiation Oncology, Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China, 510060
Not Yet Recruiting
2
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China, 510060
Actively Recruiting
Research Team
P
Pu-Yun OuYang
CONTACT
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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