Deep Learning-based sbORN Diagnostic Model
NCT Number: NCT06463392
Status: RECRUITING
Study Type: OBSERVATIONAL
Conditions: Nasopharyngeal Carcinoma|Herpesvirus 4, Human
Sponsor: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Collaborators: Sun Yat-sen University
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 312
Study Design: Observational Model: |Time Perspective: p
Start Date: 2024-07-01
Completion Date: 2030-12-31
Locations: Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, 510000, China

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.

Primary Outcome Measures: Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|Area under curve of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline
Secondary Outcome Measures: Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the deep-learning-based multimodal model., Baseline|Sensitivity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline|Specificity of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline|F1 score of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline|Positive predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline|Negative predictive value of the differential diagnosis of sbORN and NPC recurrence delivered by the radiologists., Baseline|Dice similarity coefficient of the MRI contouring between the deep-learning-based multimodal model and the radiologists., Baseline|Average surface distance of the MRI contouring between the deep-learning-based multimodal model and the radiologists., Baseline
Other Outcome Measures: The number of white blood cells in the peripheral blood., Baseline|The number of neutrophils in the peripheral blood., Baseline|The number of basophils in the peripheral blood., Baseline|The number of eosinophils in the peripheral blood., Baseline|The number of red blood cells in the peripheral blood., Baseline|The concentration of albumin in the peripheral blood., Baseline|The concentration of total protein in the peripheral blood., Baseline|The history of diabetes mellitus., Baseline|The history of hypertension., Baseline|The copy number of Epstein-Barr Virus (EBV) DNA., Baseline|The titer of EBV VCA IgA., Baseline|The titer of EBV EBNA1 IgA., Baseline|The titer of EBV EA IgA., Baseline