Artificial intelligence algorithm for preoperative prediction of FIGO stage in ovarian cancer based on clinical features integrated 18F-FDG PET/CT metabolic and radiomics features.

ABSTRACT:

Purpose

The International Federation of Gynecology and Obstetric (FIGO) stage is critical to guiding the treatments of ovarian cancer (OC). We tried to develop a model to predict the FIGO stage of OC through machine learning algorithms with patients' pretreatment clinical, positron emission tomography scan (PET/CT) metabolic, and radiomics features.

Methods

We enrolled OC patients who underwent PET/CT scans and divided them into two cohorts according to their FIGO stage. Then we manually delineated the volume of interest (VOI) and calculated PET metabolic features. Other PET/CT radiomics features were extracted by Python. We developed 11 prediction models to predict stages based on four groups of features and conducted three experiments to verify the meaning of PET/CT featur

SUBMITTER: Xu S 

PROVIDER: S-EPMC11842498 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature