Deep Learning-based sbORN Diagnostic Model

Recruiting

• Conditions: Nasopharyngeal Carcinoma; Herpesvirus 4, Human

• Registration Number: NCT06463392
• Lead Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Brief Summary

Not available

Detailed Description

Not available

Study Timeline

• Study Start: 2024-5-22
• Last Update Posted: 2 September 2024

Recruitment & Eligibility

• Status: Recruiting
• Sex: All
• Target Recruitment: 312

Inclusion Criteria

Inclusion Criteria:<br><br> - Equal to or older than 18 years old.<br><br> - A history of histologically confirmed nonkeratinizing undifferentiated<br> nasopharyngeal carcinoma.<br><br> - A history of radical radiotherapy at nasopharynx.<br><br> - Complete remission six months post radical radiotherapy according to RECIST 1.1.<br><br> - No evidence of distant metastasis upon recruitment.<br><br> - Diagnosis of sbORN given by senior radiologist with 2-4 Likert scores.<br><br> - Consent to biopsy awake or under general anesthesia.<br><br> - Consent to perform blood tests, EBV DNA, EBV IgAs, and MRI inspection of nasopharynx<br> and neck.<br><br> - With a written consent.<br><br>Exclusion Criteria:<br><br> - MRI artifacts or other factors that interfere radiological diagnosis and region of<br> interest contouring.<br><br> - Suspected lesion is not confined to nasopharynx and skull-base.

Exclusion Criteria

Not provided

Study & Design

• Study Type: Observational
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.;Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.