A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence.

Abstract

AIMS:

To develop a new package of joint model to fit longitudinal CA125 in epithelial ovarian cancer relapse.

PATIENTS & METHODS:

Included were 305 epithelial ovarian cancer patients who reached complete remission after cytoreductive surgery and first-line chemotherapy. Univariate and multivariate analysis with a joint model was performed to select independent risk factors, which were subsequently combined to predict recurrence.

RESULTS:

Independent factors were longitudinal CA125, age, stage and residual tumor size (p < 0.05). Prediction of recurrence with these factors had an average of 80.7% accuracy, 5.6-10.7% better than kinetic factors.

CONCLUSION:

The new package of joint model fits longitudinal CA125 well. Potential application can be extended to other biomarkers.

• Authors: Chung Chang, An Jen Chiang, Wei-An Chen, Hsueh-Wen Chang, Jiabin Chen
• Journal: Biomarkers in medicine (Biomark Med) Vol. 10 Issue 1 Pg. 53-61 ( 2016) ISSN: 1752-0371 [Electronic] England
• PMID: 26565119 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)

Chemical References

• CA-125 Antigen

Topics

• Adolescent
• Adult
• Aged
• Aged, 80 and over
• CA-125 Antigen (metabolism)
• Carcinoma, Ovarian Epithelial
• Female
• Humans
• Longitudinal Studies
• Middle Aged
• Models, Statistical
• Multivariate Analysis
• Neoplasms, Glandular and Epithelial (diagnosis, metabolism)
• Ovarian Neoplasms (diagnosis, metabolism)
• Prognosis
• Recurrence
• Retrospective Studies
• Risk Factors
• Young Adult