AI model predicts distant metastasis in nasopharyngeal cancer
Official title: Development and Multicenter Validation of a Deep Learning Model Based on Whole Slide Imaging and Magnetic Resonance Imaging of the Nasopharynx and Lymph Nodes to Predict Distant Metastases at Diagnosis in Nasopharyngeal Carcinoma
Run by Sun Yat-sen University · NCT06831357 · Data updated 2026-11-12
This study is testing a new AI tool to see if it can help doctors predict which patients with nasopharyngeal cancer are at high risk for spreading the disease, so they can get the right scans and avoid unnecessary ones.
Quick facts
| Study type | Observational |
|---|---|
| Sex | All |
| Enrollment | 500 (estimated) |
| Sponsor | Sun Yat-sen University Academic / other |
| Locations | 2 sites (Guangzhou, Guangdong and 1 other locations) |
| Status | Recruiting |
| Started | February 15, 2025 |
| Main results expected | December 31, 2026 |
| Study ends | December 31, 2026 |
| Registry record updated | February 25, 2025 |
| Trial ID | NCT06831357 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates for this study are patients with pathologically confirmed nasopharyngeal carcinoma at stages T3-4 or N2-3.
✕Probably not a fit
Patients with a history of other malignant tumors may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study develops and validates a deep learning model that predicts the likelihood of distant metastasis in patients with nasopharyngeal carcinoma using whole slide imaging and MRI scans. The AI model is constructed from pathology slides and MRI data of the primary tumor and is validated with data from multiple centers. It aims to identify patients at high risk for distant metastasis who may benefit from PET/CT scans while reducing unnecessary examinations for those at low risk, thereby saving medical resources and reducing costs for patients.
Why it matters
Potential benefit: If successful, this model could help streamline the diagnostic process for nasopharyngeal carcinoma, ensuring that high-risk patients receive appropriate imaging while minimizing unnecessary procedures for low-risk patients.
How similar studies have gone: Other studies utilizing AI for cancer prediction have shown promising results, indicating potential success for this approach.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: A. The primary lesion was pathologically confirmed as nasopharyngeal carcinoma (WHO classification is I, II and III); B. The stage was T3-4 or N2-3, and the nasopharynx + neck MRI plain scan and enhanced scan were performed to confirm the nasopharyngeal and cervical lymph node lesions, and PET/CT or conventional examination (chest CT plain scan + enhanced scan, upper abdominal CT or MRI plain scan + enhanced scan or abdominal color Doppler ultrasound or ultrasound angiography, and whole body bone imaging) was performed to screen for distant metastases. Exclusion Criteria: Previous history of other malignant tumors (such as other head and neck squamous cell carcinomas, thyroid cancer, breast cancer, esophageal cancer, etc.).
Where it's running
-
Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China Recruiting -
Department of Radiation Oncology, Sun Yat-sen University Cancer Center
Guangzhou, Guangdong, China Not yet recruiting
Study contacts
- Study coordinator: Pu-Yun OuYang
- Email: ouyangpy@sysucc.org.cn
- Phone: +8618565382769
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.