Artificial Intelligence Model for Growth Prediction of Ovarian Cancer Organoids

Chongqing University Cancer Hospital100 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

100

Conditions

Ovarian Cancer

Interventions

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

Eligibility

Sex: FEMALE

Medical Language ↔ Plain English

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.

Locations (1)

Chongqing Cancer Hospital

Chongqing, Chongqing Municipality, China

RECRUITING

Outcomes

Primary Outcomes

AUC of growth prediction performance using deep learning model

AUC =Area under receiver operating characteristic curve

Time frame: up to 3 years

Accuracy of growth prediction using deep learning model

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

Time frame: up to 3 years

Central Contacts

Dongling Zou, MD

CONTACT

13657690699 cqzl_zdl@163.com

Data from ClinicalTrials.gov

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