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| Name | Class |
|---|---|
| Sun Yat-sen University | OTHER |
| Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China | UNKNOWN |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Case | Histologically confirmed sbORN that meets the eligibility criteria. |
| |
| Control | Histologically confirmed NPC recurrence that meets the eligibility criteria. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No Intervention: Observational Cohort | Other | No intervention is scheduled for this observational study. |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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. |
Inclusion Criteria:
Exclusion Criteria:
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All populations that meet the eligibility criteria.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xiang-Wei Kong, Ph.D. | Contact | 0086-020-34071439 | kongxw8@mail.sysu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University | Recruiting | Guangzhou | Guangdong | 510000 | China |
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| ID | Term |
|---|---|
| D000077274 | Nasopharyngeal Carcinoma |
| D020031 | Epstein-Barr Virus Infections |
| ID | Term |
|---|---|
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
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| 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 |
| 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 |
| D009303 |
| Nasopharyngeal Neoplasms |
| D010610 | Pharyngeal Neoplasms |
| D010039 | Otorhinolaryngologic Neoplasms |
| D006258 | Head and Neck Neoplasms |
| D009371 | Neoplasms by Site |
| D009302 | Nasopharyngeal Diseases |
| D010608 | Pharyngeal Diseases |
| D009057 | Stomatognathic Diseases |
| D010038 | Otorhinolaryngologic Diseases |
| D006566 | Herpesviridae Infections |
| D004266 | DNA Virus Infections |
| D014777 | Virus Diseases |
| D007239 | Infections |
| D014412 | Tumor Virus Infections |