Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.
Skip to main content

Table 2 Univariate and multivariate Cox regression analysis for predicting PFS in EOC patients

From: A CT-based radiomics model for predicting progression-free survival in patients with epithelial ovarian cancer

Characteristics

Univariate Cox regression

Multivariate Cox regression

HR

95% CI

P-value

HR

95% CI

P-value

Age

1.027

(1.007–1.048)

0.007

1.006

(0.979–1.035)

0.659

Menopause

0.554

(0.333–0.920)

0.022

0.677

(0.340–1.348)

0.267

FIGO stage

0.495

(0.292–0.839)

0.009

0.576

(0.336–0.989)

0.045

CA125

1.000

(1.000-1.001)

0.062

   

Residual tumor

0.374

(0.243–0.575)

< 0.001

0.455

(0.288–0.718)

0.001

Tumor location

1.207

(0.774–1.883)

0.406

   

Tumor diameter

1.004

(0.999–1.009)

0.142

   

Ascites

0.695

(0.379–1.275)

0.239

   

Tumor characteristics

      

Mainly cystic

NA

 

NA

   

Solid

1.104

(0.631–1.930)

0.728

   

Mixed cystic and solid

1.231

(0.763–1.987)

0.395

   

Enhanced CT value

0.982

(0.962–1.001)

0.067

   
  1. PFS, progression-free survival; EOC, epithelial ovarian cancer; FIGO, International Federation of Gynecology and Obstetrics; CA125, carcinoma antigen 125; CT, computed tomography; HR, hazard ratio
Back to article page

BMC Cancer

ISSN: 1471-2407

Contact us