NCT06463392

Deep Learning-based sbORN Diagnostic Model

Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 312 (estimated)
• Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

Skull-base osteonecrosis (sbORN) is a severe long-term complication of nasopharyngeal carcinoma (NPC) post radiotherapy, which significantly diminish the quality of life, increase the risk of internal carotid artery rupture, and is frequently misdiagnosed as NPC recurrence. Novel diagnostic tools are therefore clinically significant. In this study, the investigators seek to ask if a deep-learning-based model shows a significantly higher sensitivity than radiologists. With a cross-sectional design, the investigators aim to recruit 312 participants in Sun Yat-sen Memorial Hospital, Guangzhou, China that meet the eligibility criteria.

Conditions

• Nasopharyngeal Carcinoma
• Herpesvirus 4, Human

Interventions

Type	Name	Description
OTHER	No Intervention: Observational Cohort	No intervention is scheduled for this observational study.

Timeline

• Start date: 2024-07-01
• Primary completion: 2029-12-31
• Completion: 2030-12-31
• First posted: 2024-06-17
• Last updated: 2024-10-01

Locations

1 site across 1 country: China