Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids

NCT06317610 | Recruiting

• 1 other identifier: CQGOG0206
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

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.

Conditions

Ovarian Cancer

Outcome Measures

Primary Outcomes (2)

• AUC of growth prediction performance using deep learning model

  AUC =Area under receiver operating characteristic curve

  up to 3 years

• Accuracy of growth prediction using deep learning model

  Accuracy=( the number of correctly classified samples)/( the number of total samples)

  up to 3 years

Interventions

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

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

Eligibility Criteria

• Gender Eligibility Details: Only women get ovarian cancer
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Chongqing University Cancer Hospital: lead

Study Sites (1)

Chongqing Cancer Hospital

Chongqing, Chongqing Municipality, 400030, China

RECRUITING

MeSH Terms

Conditions

Ovarian Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders

Study Officials

• Dongling Zou, M.D.

  Chongqing University Cancer Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Dongling Zou, MD

CONTACT

13657690699; cqzl_zdl@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 12, 2024
• First Posted: March 19, 2024
• Study Start: January 1, 2022
• Primary Completion: April 30, 2024
• Study Completion: May 30, 2024
• Last Updated: March 19, 2024

Record last verified: 2024-03

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)