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Ovarian Cancer Identification on CT Using Deep Learning

NCT06851429 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 12578

Last updated 2025-02-28

No results posted yet for this study

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.

Conditions

Sponsors & Collaborators

  • Chang Gung Memorial Hospital

    lead OTHER

Eligibility

Min Age
20 Years
Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2022-09-01
Primary Completion
2025-02-07
Completion
2025-02-28

Countries

  • Taiwan

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06851429 on ClinicalTrials.gov