Melanoma (Skin)NCT04695015
Research of Pathological Imaging Diagnosis of Ocular Tumors Based on New Artificial Intelligence Algorithm
An observational study of Melanoma (Skin) and Melanoma in Situ, run by Peking University.
Trial 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… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Melanoma (Skin), Melanoma in Situ, Nevus Eye
- How many people
- 100 participants
- Where
- No sites listed yet
- Study dates
- 31 Dec 2020to 1 Jun 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: patients from the Department of Ophthalmology, Peking University Third Hospital who has an eye tumor and undergoes surgery.
Who may be able to join
- Patients diagnosed with eye tumors and undergoing eye tumor surgery.
- Patients sign informed consent for sample collection and sample transfer agreement, and can cooperate with long-term regular follow-up requirements.
Who cannot join
- Patients who are unable to undergo tumor surgery or retain samples due to various reasons .
- Patients who are positive for hepatitis B, HIV, and syphilis.
- Patient compliance is poor.
Trial locations and contacts
No sites are listed yet.
About this study
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.
Read the 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.
- Conditions
- Melanoma (Skin), Melanoma in Situ, Nevus Eye, Sebaceous Gland Carcinoma of the Eyelid, Basal Cell Carcinoma, Squamous Cell Carcinoma in Situ
- Medical subject headings
- Melanoma, Carcinoma, Basal Cell, Squamous Intraepithelial Lesions, Eye Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to 24 weeks.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Jan 2021
- Study start31 Dec 2020 (estimated)
- Primary completion1 Jun 2022 (estimated)
- Study completion1 Jun 2022 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Melanoma (Skin)Studies of the same condition, open and under way.
- More trials run by Peking UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chun Zhang, Peking University
- Investigators
- Chun Zhang, MD/PHD, Principal investigator, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04695015
- Sponsor's protocol ID
- IRB00006761-M2020434
- Status last verified
- Jan 2021
- First submitted
- 19 Nov 2020
- Last updated
- 5 Jan 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Jan 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research of Pathological Imaging Diagnosis of Ocular Tumors Based on New Artificial Intelligence Algorithm
- ClinicalTrials.gov ID
- NCT04695015
- Acronym / sponsor ID
- IRB00006761-M2020434
- Conditions
- Melanoma (Skin), Melanoma in Situ, Nevus Eye, Sebaceous Gland Carcinoma of the Eyelid, Basal Cell Carcinoma, Squamous Cell Carcinoma in Situ
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Peking UniversityUniversity, hospital or other
- Study start
- 31 Dec 2020 (estimated)
- Primary completion
- 1 Jun 2022 (estimated)
- Study completion
- 1 Jun 2022 (estimated)
- First posted
- 5 Jan 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patients from the Department of Ophthalmology, Peking University Third Hospital who has an eye tumor and undergoes surgery.
Key inclusion criteria (2 listed)
- Patients diagnosed with eye tumors and undergoing eye tumor surgery.
- Patients sign informed consent for sample collection and sample transfer agreement, and can cooperate with long-term regular follow-up requirements.
Key exclusion criteria (3 listed)
- Patients who are unable to undergo tumor surgery or retain samples due to various reasons .
- Patients who are positive for hepatitis B, HIV, and syphilis.
- Patient compliance is poor.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Jan 2021.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Chun Zhang, MD/PHD principal investigator
Peking University Third Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Melanoma (Skin) and Melanoma in Situ, run by Peking University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 5 Jan 2021. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Chun Zhang, MD/PHD (+8618601031059, zhangc1@yahoo.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To compare the diagnostic accuracy of OPAL and IHC for melanoma and other tumors. (time frame: Up to 24 weeks.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Jan 2021.