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Updated: Feb 5, 2026

Author Spotlight: Advancing Personalized Medicine in Ovarian Cancer
Published on: February 23, 2024
Evaluation of various kinetic parameters of CA-125 in patients with advanced-stage ovarian cancer undergoing
Yong Jae Lee1, In Ha Lee1, Yun-Ji Kim1
1Department of Obstetrics and Gynecology, Institute of Women's Medical Life Science, Yonsei University College of Medicine, Seoul, Korea.
Abstract:
Various kinetic parameters of serum CA-125 have been reported to have better correlation with outcomes for patients treated with neoadjuvant chemotherapy (NAC). This study aimed to compare the available kinetic parameters of serum CA-125 in an external cohort of advanced-stage ovarian cancer. Using the cancer registry databases from the Yonsei Cancer Hospital, we retrospectively reviewed 210 patients with advanced-stage ovarian cancer, treated with NAC followed by interval debulking surgery. We compared area under the receiver-operating characteristics curves (AUCs), false negative rate, and negative predictive value (NPV) using 10 different models for optimal cytoreduction and platinum resistance. In addition, we compared incremental AUC for progression-free survival (PFS) and overall survival (OS). No gross residual tumor was observed in 37.0% and residual tumors <1 cm in 82.2% of patients. No model using CA-125 kinetic parameters had an AUC higher than >0.6 for predicting optimal cytoreduction. After adjusting for age, BMI, disease stage, and histologic subtypes, all models had an AUC >0.70 for predicting platinum resistance. However, no model had a high enough NPV (highest value = 82.0%) to avoid chemotherapy futility. For survival outcomes, no model had an incremental AUC >0.70 for predicting either PFS or OS. None of the proposed serum CA-125 kinetic parameters showed high accuracy in predicting optimal cytoreduction, platinum resistance, or survival in patients receiving NAC. For advanced-stage ovarian cancer treated with NAC, there is a need to discover reliable biomarkers to better stratify patient response groups for optimal treatment decision-making.
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