Ovarian Cancer Identification on CT Using Deep Learning

NCT06851429 | Active Not Recruiting

• 1 other identifier: 202201271B0
• Study Type: observational
• Target: 12,578
• Locations: 1 country
• Sites: 1

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.

Conditions

Ovarian Cancer

Outcome Measures

Primary Outcomes (1)

• Performance of a deep learning-based computer-aided diagnosis tool (CAT-OV) for identification of primary ovarian cancer on CT

  Sensitivity, Specificity, Accuracy, PPV, NPV, AUC

  Perioperative/Periprocedural 180 days

Study Arms (2)

control group

The control group included both benign ovarian tumors and an enriched dataset.

case group

ovarian cancer

Eligibility Criteria

• Age: 20 Years+
• Gender Eligibility Details: Patients were included if they had undergone a CT scan within 180 days prior to ovarian surgery for histopathological evaluation.
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Women who have undergone a CT scan.

You may qualify if:

• Age ≥ 20 years old.
• Female
• undergone a CT scan
• undergone a CT scan within 180 days prior to ovarian surgery for histopathological evaluation.

You may not qualify if:

• 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

Sponsors & Collaborators

• Chang Gung Memorial Hospital: lead

Study Sites (1)

Chang Gung Memorial Hospital

Taoyuan, Guishan District, 333, Taiwan

Location

MeSH Terms

Conditions

Ovarian Neoplasms

Condition Hierarchy (Ancestors)

Endocrine Gland Neoplasms; Neoplasms by Site; Neoplasms; Ovarian Diseases; Adnexal Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Neoplasms, Female; Urogenital Neoplasms; Genital Diseases; Endocrine System Diseases; Gonadal Disorders

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: February 24, 2025
• First Posted: February 28, 2025
• Study Start: September 1, 2022
• Primary Completion: February 7, 2025
• Study Completion: February 28, 2025
• Last Updated: February 28, 2025

Record last verified: 2025-02

Locations

TW (1)