Actively Recruiting

All Genders
NCT06831357

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

All Genders

Eligibility Criteria

Eligible

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
Not Eligible

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

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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

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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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