A Methodological Framework for AI-Assisted Diagnosis of Ovarian Masses Using CT and MR Imaging.

ABSTRACT: Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Can

SUBMITTER: Adusumilli P 

PROVIDER: S-EPMC11856859 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature