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
This trial does not accept healthy volunteersQualifiers
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 population
All populations that meet the eligibility criteria.
Trial design
Design model
Case-control
Time perspective
Cross-sectional
Treatments tested in this trial
No Intervention: Observational Cohort
Other interventionNo intervention is scheduled for this observational study.
Treatment groups
312 Participants
are divided into 2 treatment groupsGroup A: Case1 intervention
Group B: Control1 intervention
Trial outcomes
Primary outcomes
1
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time frame
Baseline2
Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists.
Time frame
BaselineSecondary outcomes
1
Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time frame
Baseline2
Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time frame
Baseline3
F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time frame
Baseline4
Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model.
Time frame
BaselineOther outcomes
1
The number of white blood cells in the peripheral blood.
Time frame
Baseline2
The number of neutrophils in the peripheral blood.
Time frame
Baseline3
The number of basophils in the peripheral blood.
Time frame
Baseline4
The number of eosinophils in the peripheral blood.
Time frame
BaselineSponsors and contacts
Click on the lead sponsor to view all of their trials.
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