Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids

Recruiting

• Conditions: Ovarian Cancer

• Registration Number: NCT06317610
• Lead Sponsor: Chongqing University Cancer Hospital

Brief 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.

Detailed Description

Not available

Study Timeline

• Study Start: 2022-01-01
• Status Verified: 2024-03
• Study First Submitted: 2024-03-12
• Study First Submitted QC: 2024-03-12
• Last Update Submitted: 2024-03-12
• Study First Posted: 2024-03-19
• Last Update Posted: 2024-03-19
• Primary Completion: 2024-04-30
• Study Completion: 2024-05-30

Recruitment & Eligibility

• Status: RECRUITING
• Sex: Female
• Target Recruitment: 100

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 & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy of growth prediction using deep learning model	up to 3 years	Accuracy=( the number of correctly classified samples)/( the number of total samples)
AUC of growth prediction performance using deep learning model	up to 3 years	AUC =Area under receiver operating characteristic curve

Trial Locations

Locations (1)

Chongqing Cancer Hospital; 🇨🇳 Chongqing, Chongqing, China