J Gynecol Oncol.  2014 Jan;25(1):51-57. 10.3802/jgo.2014.25.1.51.

A longitudinal analysis with CA-125 to predict overall survival in patients with ovarian cancer

• Chiang AJ
• Chen J
• Chung YC
• Huang HJ
• Liou WS
• Chang C

• KMID: 1811188
• DOI: http://doi.org/10.3802/jgo.2014.25.1.51

Abstract

OBJECTIVE
The objective of this study was to explore the association of longitudinal CA-125 measurements with overall survival (OS) time by developing a flexible model for patient-specific CA-125 profiles, and to provide a simple and reliable prediction of OS.
METHODS
A retrospective study was performed on 275 patients with ovarian cancer who underwent at least one cycle of primary chemotherapy in our institute. Serial measurements of patients' CA-125 levels were performed at different frequencies according to their clinical plans. A statistical model coupling the Cox proportional hazards and the mixed-effects models was applied to determine the association of OS with patient-specific longitudinal CA-125 values. Stage and residual tumor size were additional variables included in the analysis.
RESULTS
A total of 1,601 values of CA-125 were included. Longitudinal CA-125 levels, stage, and the residual tumor size were all significantly associated with OS. A patient-specific survival probability could be calculated. Validation showed that, in average, 85.4% patients were correctly predicted to have a high or low risk of death at a given time point. Comparison with a traditional model using CA-125 half-life and time to reach CA-125 nadir showed that the longitudinal CA-125 model had an improved predicative value.
CONCLUSION
Longitudinal CA-125 values, measured from the diagnosis of ovarian cancer to the completion of primary chemotherapy, could be used to reliably predict OS after adjusting for the stage and residual tumor disease. This model could be potentially useful in clinical counseling of patients with ovarian cancer.

Keyword

CA-125; Longitudinal analysis; Ovarian cancer; Overall survival; Prediction

MeSH Terms

Counseling
Diagnosis
Drug Therapy
Half-Life
Humans
Models, Statistical
Neoplasm, Residual
Ovarian Neoplasms*
Retrospective Studies

Figure

• 

  Fig. 1 Typical examples of patient-specific CA-125 profiles. Different patients had different numbers of CA-125 measurements taken at different time points spanning different time frames. The profile of patient A contained only 3 measurements in the first two months following admission. Patient B had 8 CA-125 values taken throughout the course from surgery to completion of the primary chemotherapy (approximately 6 months). Patient C had four CA-125 measurements in the first three months and her preoperative CA-125 value was missing. Patient D had three CA-125 observations throughout the course from surgery to completion of the primary chemotherapy (around 6 months).

• 

  Fig. 2 Residual plots of the joint model. (A) The subject specific residual plot for the validity of the mixed-effects model. The red line is the lowess curve of the fitted values. It is very close to the horizontal line from point 0 (the dotted line), suggesting a good fit. (B) The Cox-Snell residual plot for the accuracy of the second part of the joint model. The y axis is the fitted survival time (Kaplan Meier estimates). The solid black line is the Kaplan-Meier curve of the residuals, which corresponds to the ideal curve (the red line) quite well, suggesting a good fit of the model. The two dotted lines give the 95% confidence interval.

• 

  Fig. 3 The lowess curves of CA-125 profiles according to high and low risk patients at 3-year survival. Those who died within 3 years after surgery are considered high risk (n=41), and those who survived for at least 3 years are considered low risk (n=177). Patients who were censored within 3 years were excluded from the curves. All the CA-125 values of each entire group were locally smoothened to generate a curve that showed the overall distribution of CA-125 profiles.

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