Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids

March 12, 2024 updated by: Chongqing University Cancer Hospital

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

100

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Chongqing
      • Chongqing, Chongqing, China, 400030
        • Recruiting
        • Chongqing Cancer Hospital
        • Principal Investigator:
          • Dongling Zou, M.D.
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with epithelial ovarian cancer received biopsy or puncture to obtain tumor tissues or malignant effusion

Description

Inclusion Criteria:

  • 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.

Exclusion Criteria:

  • Non-epithelial ovarian cancer
  • No sufficient amount of tumor tissues or malignant effusion for organoids establishment.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AUC of growth prediction performance using deep learning model
Time Frame: up to 3 years
AUC =Area under receiver operating characteristic curve
up to 3 years
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)
up to 3 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Chongqing University Cancer Hospital

Investigators

  • Principal Investigator: Dongling Zou, M.D., Chongqing University Cancer Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2022

Primary Completion (Estimated)

April 30, 2024

Study Completion (Estimated)

May 30, 2024

Study Registration Dates

First Submitted

March 12, 2024

First Submitted That Met QC Criteria

March 12, 2024

First Posted (Actual)

March 19, 2024

Study Record Updates

Last Update Posted (Actual)

March 19, 2024

Last Update Submitted That Met QC Criteria

March 12, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • CQGOG0206

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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