Radiomics Signatures Based on Computed Tomography for Noninvasive Prediction of CXCL10 Expression and Prognosis in Ovarian Cancer.

ABSTRACT:

Background

Ovarian cancer (OC) is an aggressive gynecological tumor usually diagnosed with malignant ascites and even observed widespread metastasis or distant spread.

Aims

We aimed to develop and identify radiomics models according to computed tomography (CT) for preoperative prediction of CXCL10 expression and prognosis in patients with OC.

Methods

Genomic data with CT images and corresponding clinicopathological parameters were extracted from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA). To analyze the prognosis, we carried out the univariate Cox regression analysis (UCRA), multivariate Cox regression analysis (MCRA), and Kaplan-Meier (KM) analysis. For the data reduction, logistic regression, operator regression, least absolute shrinkage se

SUBMITTER: Wang X 

PROVIDER: S-EPMC11499071 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature