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Deep Learning-based sbORN Diagnostic Model
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
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.
Official title: Development of Deep-Learning-Based Multimodal Post Radiotherapy Skull-Base Osteonecrosis and Recurrence of Nasopharyngeal Carcinoma Differential Diagnostic Model
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
OBSERVATIONAL
Enrollment
312
Start Date
2024-07-01
Completion Date
2030-12-31
Last Updated
2024-10-01
Healthy Volunteers
No
Interventions
No Intervention: Observational Cohort
No intervention is scheduled for this observational study.
Locations (1)
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Guangzhou, Guangdong, China