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Updated: Mar 30, 2026
Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence
Chung Chang1, An Jen Chiang2,3,4, Wei-An Chen1
1Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan, Republic of China.
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.
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