Development and Validation of a Deep Learning Model to Predict Distant Metastases in Nasopharyngeal Carcinoma Using Whole Slide Imaging and MRI

NCT ID: NCT06831357

Last Updated: 2025-02-25

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-02-15
• Study Completion Date: 2026-12-31

Brief Summary

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.

Detailed Description

An AI model was constructed based on HE-stained pathological sections of the primary lesion and MRI of the nasopharynx and neck to predict the probability of distant metastasis at the first visit, and the AI model was fully verified by multicenter data; the AI model was applied to T3-4 or N2-3 patients who were recommended to undergo PET/CT examination according to the NCCN and CSCO guidelines, and the threshold of the AI model when the negative predictive value for predicting M0 was not less than 95% was determined, providing theoretical support for patients predicted by AI to be exempted from PET/CT examination.

Conditions

Nasopharyngeal Cancinoma (NPC); Distant Metastasis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Prospective Validation Cohort

Prospective patient enrollment to validate the diagnostic efficacy of the AI model

No interventions assigned to this group

Eligibility 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.).

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

First Affiliated Hospital, Sun Yat-Sen University

OTHER

Sponsor Role: collaborator

Fifth Affiliated Hospital, Sun Yat-Sen University

OTHER

Sponsor Role: collaborator

Affiliated Cancer Hospital & Institute of Guangzhou Medical University

OTHER

Sponsor Role: collaborator

The Affiliated Panyu Center Hospital of Guangzhou Medical University

UNKNOWN

Sponsor Role: collaborator

Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

OTHER

Sponsor Role: collaborator

Qingyuan People's Hospital

OTHER

Sponsor Role: collaborator

Sun Yat-sen University

OTHER

Sponsor Role: lead

Responsible Party

Pu-Yun OuYang

Associate Chief Physician

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

Department of Radiation Oncology, Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, China

Site Status: NOT_YET_RECRUITING

Sun Yat-sen University Cancer Center

Guangzhou, Guangdong, China

Site Status: RECRUITING

Countries

China

Central Contacts

Pu-Yun OuYang

Role: CONTACT

ouyangpy@sysucc.org.cn

+8618565382769

Facility Contacts

Pu-Yun OuYang

Role: primary

ouyangpy@sysucc.org.cn

86+020-87342925

Pu-Yun OuYang

Role: primary

ouyangpy@sysucc.org.cn

+86 18565382769

Pu-Yun OuYang

Role: backup

ouyangpy@sysucc.org.cn

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Other Identifiers

B2025-062-01

Identifier Type: -

Identifier Source: org_study_id