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The purpose of this study is to establish a standardized process for obtaining digital pathological image information of ocular tumors; use modern pathological techniques to obtain the co-expression information of multiple biomarkers in the pathological tissues of ocular tumors, and finally construct standardized digital ocular tumors with biomarkers Pathology image database.
This study is a prospective study. Patients with common and representative ocular tumors in the Department of Ophthalmology, Peking University Third Hospital, will be selected and enrolled after informed consent to collect basic clinical information, preoperative blood samples, and ocular tumors Obtain pathological image annotation data and genomics-related data from ocular tumor tissue specimens, use blood samples for genomics information analysis, provide multi-dimensional data for the development of artificial intelligence algorithms, and establish artificial intelligence-assisted image data for eye tumors Standardize the process and establish a multi-modal ocular tumor standardized database of "clinical information-tissue samples-pathological images-genomics data". The database and the diagnosis system are correlated with each other to provide optimal image data for later machine learning and related algorithm establishment, and finally the investigators will be completed the design of a new artificial intelligence-assisted diagnosis system for eye tumors.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Melanoma and Nevus | Patients diagnosed with melanoma or/and nevus on the skin around the eye before surgery. | ||
| Basal cell carcinoma;Squamous cell carcinoma;Sebaceous gland carcinoma | Patients diagnosed with basal cell carcinoma, squamous cell carcinoma, sebaceous gland carcinoma before surgery. |
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| Measure | Description | Time Frame |
|---|---|---|
| To compare the diagnostic accuracy of OPAL and IHC for melanoma and other tumors. | The result of OPAL automatic analysis will be compared with IHC manual counting analysis.The accuracy of the study will be declared "success" if OPAL automatic analysis meet more than 85% of the manual count for all antibody. | Up to 24 weeks. |
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Inclusion Criteria:
Exclusion Criteria:
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patients from the Department of Ophthalmology, Peking University Third Hospital who has an eye tumor and undergoes surgery.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Chun Zhang, MD/PHD | Contact | +8618601031059 | zhangc1@yahoo.com | |
| Defu Wu, master | Contact | +8613733899823 | 65319052@163.com |
| Name | Affiliation | Role |
|---|---|---|
| Chun Zhang, MD/PHD | Peking University Third Hospital | Principal Investigator |
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After the blood samples are treated with EDTA anti coagulation, the second-generation sequencing is performed from the DNA level and the RNA level to collect relevant genomics information, and establish an ocular tumor fusion genome database. Investigators will establish a complete sequencing data annotation database based on the existing tumor sequencing data annotation and analysis of soft nail sets and tumor sequencing data related public databases (such as the cosmic database).
| ID | Term |
|---|---|
| D008545 | Melanoma |
| D002280 | Carcinoma, Basal Cell |
| D000081483 | Squamous Intraepithelial Lesions |
| D005134 | Eye Neoplasms |
| ID | Term |
|---|---|
| D018358 | Neuroendocrine Tumors |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009369 | Neoplasms |
| D009380 | Neoplasms, Nerve Tissue |
| D018326 | Nevi and Melanomas |
| D012878 | Skin Neoplasms |
| D009371 | Neoplasms by Site |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
| D002277 | Carcinoma |
| D009375 | Neoplasms, Glandular and Epithelial |
| D018295 | Neoplasms, Basal Cell |
| D065308 | Morphological and Microscopic Findings |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D005128 | Eye Diseases |
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