Ovarian Cancer Identification on CT Using Deep Learning

Brief summary (registry, in English)

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.

What you commit to

• Study type: Observational
• Planned participants: 12578
• From 1 Sept 2022 to the primary completion date, 7 Feb 2025
• 2 sites in 1 country
• Visits and tests: ask the trial team, the registry does not list them

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Who cannot take part

Copied verbatim from ClinicalTrials.gov and grouped by topic. Only the trial team decides who can take part.

Who the study is looking for

Age in the registry: 20 years to - · Sex: Female · Accepts healthy volunteers

Full criteria on ClinicalTrials.gov

Sites (2)

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