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Research of Pathological Imaging Diagnosis of Ocular Tumors Based on New Artificial Intelligence Algorithm

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ClinicalTrials.gov Identifier: NCT04695015
Recruitment Status : Not yet recruiting
First Posted : January 5, 2021
Last Update Posted : January 5, 2021
Sponsor:
Information provided by (Responsible Party):
Chun Zhang, Peking University

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.

Condition or disease
Melanoma (Skin) Melanoma in Situ Nevus Eye Sebaceous Gland Carcinoma of the Eyelid Basal Cell Carcinoma Squamous Cell Carcinoma in Situ Ocular Tumor

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.

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Study Type : Observational
Estimated Enrollment : 100 participants
Observational Model: Case-Control
Time Perspective: Prospective
Official Title: Research of Pathological Imaging Diagnosis of Ocular Tumors Based on New Artificial Intelligence Algorithm
Estimated Study Start Date : December 31, 2020
Estimated Primary Completion Date : June 1, 2022
Estimated Study Completion Date : June 1, 2022

Resource links provided by the National Library of Medicine

MedlinePlus Genetics related topics: Melanoma
MedlinePlus related topics: Melanoma

Group/Cohort
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.



Primary Outcome Measures :
  1. To compare the diagnostic accuracy of OPAL and IHC for melanoma and other tumors. [ Time Frame: 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.


Biospecimen Retention:   Samples With DNA
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).


Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
patients from the Department of Ophthalmology, Peking University Third Hospital who has an eye tumor and undergoes surgery.
Criteria

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.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04695015


Contacts
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Contact: Chun Zhang, MD/PHD +8618601031059 zhangc1@yahoo.com
Contact: Defu Wu, master +8613733899823 65319052@163.com

Sponsors and Collaborators
Peking University
Investigators
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Principal Investigator: Chun Zhang, MD/PHD Peking University Third Hospital
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Responsible Party: Chun Zhang, professor, Peking University
ClinicalTrials.gov Identifier: NCT04695015    
Other Study ID Numbers: IRB00006761-M2020434
First Posted: January 5, 2021    Key Record Dates
Last Update Posted: January 5, 2021
Last Verified: January 2021

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Carcinoma
Melanoma
Carcinoma, Basal Cell
Carcinoma in Situ
Eye Neoplasms
Adenocarcinoma, Sebaceous
Neoplasms, Glandular and Epithelial
Neoplasms by Histologic Type
Neoplasms
Neuroendocrine Tumors
Neuroectodermal Tumors
Neoplasms, Germ Cell and Embryonal
Neoplasms, Nerve Tissue
Nevi and Melanomas
Neoplasms, Basal Cell
Neoplasms by Site
Eye Diseases
Adenocarcinoma
Neoplasms, Adnexal and Skin Appendage