Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning.

ABSTRACT: Epithelial ovarian cancer (EOC) presents considerable difficulties in prognostication and treatment strategy development. Bevacizumab, an anti-angiogenic medication, has demonstrated potential in enhancing progression-free survival (PFS) in EOC patients. Nevertheless, the identification of individuals at elevated risk of disease progression following treatment remains a challenging task. This study was to develop and validate a deep learning (DL) model using retrospectively collected computed tomography (CT) plain scans of inoperable and recurrent EOC patients receiving bevacizumab treatment diagnosed between January 2013 and January 2024. A total of 525 patients from three different institutions were retrospectively included in the study and divided into training set (N = 400), internal t

SUBMITTER: Huang X 

PROVIDER: S-EPMC11399346 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature