755P A machine learning approach applied to gynaecological ultrasound to predict progression-free survival in ovarian cancer patients

Abstract

Background: Ultrasound (US) is a cheap, non-invasive and well-recognized image modality for diagnosing and assessing ovarian cancer (OC). However, approximately 18% to 31% of adnexal lesions detected on US remain indeterminate. Machine learning (ML) is a promising tool for the implementation of complex multi-parametric algorithms. Despite the standardization of features capable of supporting the discrimination of ovarian masses into benign and malignant, there is the lack of accurate predictive modeling based on US examination for progression-free survival (PFS).