Deep Learning Tool Reviews CT Scans for Ovarian Cancer
Tests an AI tool that reads CT scans to help spot ovarian cancer in adults.
Plain-language title generated from the registry record
Registry title: Ovarian Cancer Identification on CT Using Deep Learning
Key facts
- Where
- Taoyuan and Taoyuan City, TaiwanSee the 2 sites
Where in Taiwan
- Department of Medical Imaging and Intervention, Chang Gung Memorial HospitalTaoyuan, TaiwanTaking people
- Chang Gung Memorial HospitalTaoyuan City, TaiwanNo longer taking part
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Chang Gung Memorial Hospital
- Trial code
- NCT06851429
- Record updated
- 12 August 2026
- Original description
Ovarian cancer remains the deadliest gynecologic malignancy, with poor survival rates largely due to late-stage diagnosis. Early detection is crucial, yet no universally accepted screening method exists. Current imaging techniques and biomarkers, such as CA-125, have limitations in specificity and sensitivity. This study aims to develop and evaluate a deep learning-based computer-aided diagnosis tool (CAT-OV), for ovarian cancer detection using CT imaging. The system integrates a Body Part Regression (BPR) model for pelvic localization and a Multiple Instance Learning (MIL) ensemble classifier for cancer prediction. The model was trained and validated using retrospective datasets from Taiwan, the United States, and a nationwide real-world cohort. Stringent preprocessing and quality control measures were implemented to enhance model accuracy. Results highlight the potential of AI-driven CT screening in improving early detection, though further validation is needed for clinical adoption.Read full original descriptionShow less
Development and Validation of a Deep Learning Model for Ovarian Cancer Identification on CT: A Nationwide Population-Based and International Study
Ovarian cancer remains the deadliest gynecologic malignancy, with poor survival rates largely due to late-stage diagnosis. Early detection is crucial, yet no universally accepted screening method exists. Current imaging techniques and biomarkers, such as CA-125, have limitations in specificity and sensitivity. This study aims to develop and evaluate a deep learning-based computer-aided diagnosis tool (CAT-OV), for ovarian cancer detection using CT imaging. The system integrates a Body Part Regression (BPR) model for pelvic localization and a Multiple Instance Learning (MIL) ensemble classifier for cancer prediction. The model was trained and validated using retrospective datasets from Taiwan, the United States, and a nationwide real-world cohort. Stringent preprocessing and quality control measures were implemented to enhance model accuracy. Results highlight the potential of AI-driven CT screening in improving early detection, though further validation is needed for clinical adoption.
Inclusion: Age ≥ 20 years old; Female; undergone a CT scan; undergone a CT scan within 180 days prior to ovarian surgery for histopathological evaluation.
Exclusion: Age < 20 years old; Non-female; Non-CT imaging; Incorrect image orientation; Number of slices < 10; Slice thickness >10 mm or < 1 mm; Unsuccessful DICM-to-NIfTI; Pelvic subvolume extraction failed; Non-contrast CT scans; Metallic artifacts; Inconclusive cases.