Ovarian Cancer Identification on CT Using Deep Learning

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.

NCT Number	NCT06851429
Lead Sponsor	Chang Gung Memorial Hospital
Conditions	Ovarian Cancer
Enrollment	12,578 participants
Start Date	2022-09-01
Primary Completion	2025-02-07 (estimated)
Study Completion	2028-08-31 (estimated)
Updated on ClinicalTrials.gov	2026-08-12