AI model predicts distant metastasis in nasopharyngeal cancer
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
Sun Yat-sen University · NCT06831357
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 |
|---|---|
| Enrollment | 500 (estimated) |
| Sex | All |
| Sponsor | Sun Yat-sen University (other) |
| Locations | 2 sites (Guangzhou, Guangdong and 1 other locations) |
| Trial ID | NCT06831357 on ClinicalTrials.gov |
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.
Who should consider this trial
Good fit: Ideal candidates for this study are patients with pathologically confirmed nasopharyngeal carcinoma at stages T3-4 or N2-3.
Not a fit: Patients with a history of other malignant tumors may not benefit from this study.
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 performed: Other studies utilizing AI for cancer prediction have shown promising results, indicating potential success for this approach.
Eligibility criteria
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 this trial is running
Guangzhou, Guangdong and 1 other locations
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center — Guangzhou, Guangdong, China (NOT_YET_RECRUITING)
- Sun Yat-sen University Cancer Center — Guangzhou, Guangdong, China (RECRUITING)
Study contacts
- Study coordinator: Pu-Yun OuYang
- Email: ouyangpy@sysucc.org.cn
- Phone: +8618565382769
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Nasopharyngeal Cancinoma, Distant Metastasis, PET/CT, MRI, whole slide imaging, deep learning model