Deep Learning-based sbORN Diagnostic Model
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorSun Yat-Sen Memorial Hospital of Sun Yat-Sen University
About this trial
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.
Eligibility criteria
Qualifiers
Equal to or older than 18 years old.
A history of histologically confirmed nonkeratinizing undifferentiated nasopharyngeal carcinoma.
A history of radical radiotherapy at nasopharynx.
Complete remission six months post radical radiotherapy according to RECIST 1.1.
Disqualifiers
MRI artifacts or other factors that interfere radiological diagnosis and region of interest contouring.
Suspected lesion is not confined to nasopharynx and skull-base.
Trial design
Treatments tested in this trial
- No Intervention: Observational Cohort
Treatment groups
312 Participants
are divided into 2 treatment groupsSponsors and collaborators
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Lead sponsor
Sun Yat-sen University
Collaborator
Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China
Collaborator
Source ClinicalTrials.gov