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Recruiting NCT06851429

NCT06851429 Ovarian Cancer Identification on CT Using Deep Learning

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Clinical Trial Summary
NCT ID NCT06851429
Status Recruiting
Phase
Sponsor Chang Gung Memorial Hospital
Condition Ovarian Cancer
Study Type OBSERVATIONAL
Enrollment 12,578 participants
Start Date 2022-09-01
Primary Completion 2025-02-07

Eligibility & Interventions

Sex Female only
Min Age 20 Years
Max Age N/A
Study Type OBSERVATIONAL

Eligibility Fast-Check

Enter your details for a quick preliminary check. This does not replace medical advice.

What to Expect as a Participant

This is an observational study. You will not receive an experimental treatment; researchers will collect data based on your existing condition or standard treatment.

This trial targets 12,578 participants in total. It began in 2022-09-01 with a primary completion date of 2025-02-07.

⚠ This information is for research awareness only. Always consult your physician before joining any clinical trial. Participation is voluntary and you may withdraw at any time.

Brief Summary

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.

Eligibility Criteria

Inclusion Criteria: 1. Age ≥ 20 years old. 2. Female 3. undergone a CT scan 4. undergone a CT scan within 180 days prior to ovarian surgery for histopathological evaluation. Exclusion Criteria: 1. Age \< 20 years old. 2. Non-female 3. Non-CT imaging 4. Incorrect image orientation 5. Number of slices \< 10 6. Slice thickness \>10 mm or \< 1 mm 7. Unsuccessful DICM-to-NIfTI 8. Pelvic subvolume extraction failed 9. Non-contrast CT scans 10. Metallic artifacts 11. Inconclusive cases

Contact & Investigator

Central Contact

Gigin Lin, MD, PhD

✉ giginlin@cgmh.org.tw

📞 886-3-3281200

Principal Investigator

Gigin Lin, MD, PhD

PRINCIPAL INVESTIGATOR

Chang Gung Memorial Hospital

Frequently Asked Questions

Who can join the NCT06851429 clinical trial?

This trial is open to female participants only, aged 20 Years or older, studying Ovarian Cancer. Full inclusion and exclusion criteria are listed in the Eligibility Criteria section. Always confirm your eligibility with the research team before applying.

Is NCT06851429 currently recruiting?

Yes, NCT06851429 is actively recruiting participants. Contact the research team at giginlin@cgmh.org.tw for enrollment information.

Where is the NCT06851429 trial being conducted?

This trial is being conducted at Taoyuan City, Taiwan, Taoyuan, Taiwan.

Who is sponsoring the NCT06851429 clinical trial?

NCT06851429 is sponsored by Chang Gung Memorial Hospital. The principal investigator is Gigin Lin, MD, PhD at Chang Gung Memorial Hospital. The trial plans to enroll 12,578 participants.

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ClinicalMetric — Independent clinical trial intelligence platform. Not affiliated with NIH, ClinicalTrials.gov, the U.S. FDA, or any pharmaceutical company, hospital, or clinical research organization. Trial data is sourced from ClinicalTrials.gov for informational purposes only and does not constitute medical advice. Do not make any treatment, enrollment, or health decisions based solely on information found here — always consult a qualified healthcare professional. Full Disclaimer  ·  Last Reviewed: September 2026  ·  Data Methodology