Clinical trial · Observational
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
NCT06317610CI-TRIAL-00075204unknownClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Ovarian Cancer | Malignant Ovarian Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| growth prediction of ovarian cancer organoids in the frame of bright field image by leveraging AI | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (2)
- measure
- AUC of growth prediction performance using deep learning model
- timeFrame
- up to 3 years
- description
- AUC =Area under receiver operating characteristic curve
- measure
- Accuracy of growth prediction using deep learning model
- timeFrame
- up to 3 years
- description
- Accuracy=( the number of correctly classified samples)/( the number of total samples)
Eligibility
Eligibility (as posted)
- Sex
- Female
Show eligibility criteria text
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.
References
Publications (0)
Data not yet available
No reference posted for this study.