Ovarian CancerNCT06317610
Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids
An observational study of Ovarian Cancer, run by Chongqing University Cancer Hospital at 1 site in China.
Trial summary
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Ovarian Cancer
- What is being tested?
- growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI
- How many people
- 100 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2022to 30 May 2024
- 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
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with epithelial ovarian cancer received biopsy or puncture to obtain tumor tissues or malignant effusion
Who may be able to join
- Patients must have histologically confirmed diagnosis of epithelial ovarian cancer
- Patients received biopsy or puncture to obtain tumor tissues or malignant effusion
- Patients voluntarily participated in the study and signed informed consent.
Who cannot join
- Non-epithelial ovarian cancer
- No sufficient amount of tumor tissues or malignant effusion for organoids establishment.
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Chongqing Cancer Hospital
Chongqing, Chongqing Municipality, 400030
Dongling Zou, M.D., 13657690699, cqzl_zdl@163.com
Dongling Zou, M.D.
About this study
Official title: Development and Validation of Growth Prediction Model for Ovarian Cancer Organoids Based on Bright Field Image and Deep Learning
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids.
- Conditions
- Ovarian Cancer
- Being tested
- growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI
- Medical subject headings
- Ovarian Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AIOther
biopsy or puncture: Patients received biopsy or puncture to obtain tumor tissues or Malignant effusion for organoids establishment
What is being measured
The primary outcome is the main question the study is designed to answer.
AUC of growth prediction performance using deep learning model
AUC =Area under receiver operating characteristic curve
Measured: up to 3 years
Accuracy of growth prediction using deep learning model
Accuracy=( the number of correctly classified samples)/( the number of total samples)
Measured: up to 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Mar 2024
- Study start1 Jan 2022
- Primary completion30 Apr 2024 (estimated)
- Study completion30 May 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ovarian CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Chongqing University Cancer Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Dongling Zou, M.D., Principal investigator, Chongqing University Cancer Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06317610
- Sponsor's protocol ID
- CQGOG0206
- Status last verified
- Mar 2024
- First submitted
- 12 Mar 2024
- Last updated
- 19 Mar 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 Mar 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of Growth Prediction Model for Ovarian Cancer Organoids Based on Bright Field Image and Deep Learning
- ClinicalTrials.gov ID
- NCT06317610
- Acronym / sponsor ID
- CQGOG0206
- Conditions
- Ovarian Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 100 participants (estimated)
- Lead sponsor
- Chongqing University Cancer HospitalUniversity, hospital or other
- Study start
- 1 Jan 2022 (actual)
- Primary completion
- 30 Apr 2024 (estimated)
- Study completion
- 30 May 2024 (estimated)
- First posted
- 19 Mar 2024 (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 present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids.
Study design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with epithelial ovarian cancer received biopsy or puncture to obtain tumor tissues or malignant effusion
Key inclusion criteria (3 listed)
- Patients must have histologically confirmed diagnosis of epithelial ovarian cancer
- Patients received biopsy or puncture to obtain tumor tissues or malignant effusion
- Patients voluntarily participated in the study and signed informed consent.
Key exclusion criteria (2 listed)
- Non-epithelial ovarian cancer
- No sufficient amount of tumor tissues or malignant effusion for organoids establishment.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AIbiopsy or puncture: Patients received biopsy or puncture to obtain tumor tissues or Malignant effusion for organoids establishment | Other | – |
Primary outcome
AUC of growth prediction performance using deep learning model
Time frame: up to 3 years
AUC =Area under receiver operating characteristic curve
Accuracy of growth prediction using deep learning model
Time frame: up to 3 years
Accuracy=( the number of correctly classified samples)/( the number of total samples)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Mar 2024.
- Individual participant dataSharing planned: No
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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.
Dongling Zou, M.D. principal investigator
Chongqing University Cancer Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Ovarian Cancer, run by Chongqing University Cancer Hospital at 1 site in China.
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 3 inclusion and 2 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 19 Mar 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Dongling Zou, MD (13657690699, cqzl_zdl@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUC of growth prediction performance using deep learning model (time frame: up to 3 years). The study lists 2 primary outcomes in all. 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 19 Mar 2024.