Basal Cell Carcinoma, Melanoma in Situ, Melanoma (Skin), Nevus Eye, Ocular Tumor, Sebaceous Gland Carcinoma of the Eyelid, Squamous Cell Carcinoma in Situ
Conditions
Brief summary
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.
Detailed description
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.
Interventions
None listed
Sponsors
Peking University
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. Patients diagnosed with eye tumors and undergoing eye tumor surgery. 2. Patients sign informed consent for sample collection and sample transfer agreement, and can cooperate with long-term regular follow-up requirements.
Exclusion criteria
1. Patients who are unable to undergo tumor surgery or retain samples due to various reasons . 2. Patients who are positive for hepatitis B, HIV, and syphilis. 3. Patient compliance is poor.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To compare the diagnostic accuracy of OPAL and IHC for melanoma and other tumors. | Up to 24 weeks. | 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. |
Contacts
Primary ContactChun Zhang, MD/PHD
Backup ContactDefu Wu, master
Outcome results
None listed