Ovarian Cancer Identification on CT Using Deep Learning

Condition(s) studied

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.

Eligibility

Primary outcome measure(s)

• Performance of a deep learning-based computer-aided diagnosis tool (CAT-OV) for identification of primary ovarian cancer on CT — Perioperative/Periprocedural 180 days
  Sensitivity, Specificity, Accuracy, PPV, NPV, AUC

Trial sites (2)

More Chang Gung Memorial Hospital trials in Taiwan

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