RecruitingNCT06317610

Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids

Development and Validation of Growth Prediction Model for Ovarian Cancer Organoids Based on Bright Field Image and Deep Learning


Sponsor

Chongqing University Cancer Hospital

Enrollment

100 participants

Start Date

Jan 1, 2022

Study Type

OBSERVATIONAL

Conditions

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.


Eligibility

Sex: FEMALE

Plain Language Summary

Simplified for easier understanding

This study uses artificial intelligence to predict how cancer cells from ovarian cancer patients will grow in lab-grown mini-tumors called organoids. The goal is to use AI to help predict a patient's response to treatment, potentially improving how doctors choose therapies. **You may be eligible if...** - You have a confirmed diagnosis of epithelial ovarian cancer (the most common type) - You have had a biopsy or procedure to collect tumor tissue or fluid containing cancer cells - You are willing to participate and have signed the informed consent **You may NOT be eligible if...** - Your ovarian cancer is a non-epithelial type - There is not enough tumor tissue or fluid available to grow the mini-tumors needed for the study Talk to your doctor to see if this trial is right for you.

This summary was AI-generated to explain the trial in plain language. It is not medical advice. Always discuss eligibility with your doctor before enrolling in a clinical trial.

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Interventions

OTHERgrowth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI

biopsy or puncture: Patients received biopsy or puncture to obtain tumor tissues or Malignant effusion for organoids establishment


Locations(1)

Chongqing Cancer Hospital

Chongqing, Chongqing Municipality, China

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NCT06317610


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