Real-World Validation of the KELIM Score for Predicting Chemosensitivity and Survival in Advanced Ovarian Cancer

Aim: We evaluated the prognostic significance of the KELIM score, which is derived from CA-125 kinetics, in predicting survival outcomes among patients with advanced epithelial ovarian cancer in a real-world clinical context. Material and Methods: This retrospective study analyzed 238 patients with stage III-IV ovarian cancer treated between 2017-2024. KELIM scores, calculated from CA-125 kinetics during the first 100 treatment days, were categorized as high (favorable, >0.9) or low (unfavorable, ≤0.9) based on the ROC analysis. Survival outcomes were assessed using Kaplan-Meier and Cox regression analyses. Results: Patients with low KELIM scores had significantly shorter median overall survival (33 vs. 68 months, p < 0.001) and progression-free survival (11 vs. 18 months, p < 0.001) than those with high scores. Multivariate Cox regression analysis identified a low KELIM score as a significant predictor of poor overall survival (HR, 4.062; 95% CI, 2.711–6.087; p < 0.001). Additionally, disease progression was more frequent in patients with a low KELIM score (89.4%) than in those with a high KELIM score (69.9%, p = 0.001). Conclusion: The KELIM score serves as a practical and accessible prognostic biomarker for advanced ovarian cancer with the potential to inform early treatment decisions for patients who are unlikely to benefit from standard chemotherapy. Further prospective studies are warranted to validate their roles in personalized therapeutic strategies.

Authors

Institutions

Publication Details

Journal
Turkish Journal of Clinics and Laboratory
Published
2025-12-31
DOI
https://doi.org/10.18663/tjcl.1802567
Citations
1
Primary Topic
Ovarian cancer diagnosis and treatment
Type
article
Field-Weighted Citation Impact
1.70
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Real-World Validation of the KELIM Score for Predicting Chemosensitivity and Survival in Advanced Ovarian Cancer

Ece Baydar, Umut Kefeli, Kazım Uygun, Yasemin Bakkal Temi et al.
1 citations
Turkish Journal of Clinics and Laboratory
Ovarian cancer diagnosis and treatment
1.70
article

Real-World Validation of the KELIM Score for Predicting Chemosensitivity and Survival in Advanced Ovarian Cancer

Ece Baydar, Umut Kefeli, Kazım Uygun, Yasemin Bakkal Temi, İlkay Çitakkul, Devrim Çabuk, Sibel Balcı
article en
1 citations

Abstract

Aim: We evaluated the prognostic significance of the KELIM score, which is derived from CA-125 kinetics, in predicting survival outcomes among patients with advanced epithelial ovarian cancer in a real-world clinical context. Material and Methods: This retrospective study analyzed 238 patients with stage III-IV ovarian cancer treated between 2017-2024. KELIM scores, calculated from CA-125 kinetics during the first 100 treatment days, were categorized as high (favorable, >0.9) or low (unfavorable, ≤0.9) based on the ROC analysis. Survival outcomes were assessed using Kaplan-Meier and Cox regression analyses. Results: Patients with low KELIM scores had significantly shorter median overall survival (33 vs. 68 months, p < 0.001) and progression-free survival (11 vs. 18 months, p < 0.001) than those with high scores. Multivariate Cox regression analysis identified a low KELIM score as a significant predictor of poor overall survival (HR, 4.062; 95% CI, 2.711–6.087; p < 0.001). Additionally, disease progression was more frequent in patients with a low KELIM score (89.4%) than in those with a high KELIM score (69.9%, p = 0.001). Conclusion: The KELIM score serves as a practical and accessible prognostic biomarker for advanced ovarian cancer with the potential to inform early treatment decisions for patients who are unlikely to benefit from standard chemotherapy. Further prospective studies are warranted to validate their roles in personalized therapeutic strategies.

Turkish Journal of Clinics and LaboratoryVol. 16(4)
Kocaeli Üniversitesi (TR)
No poverty
Openalex Percentile: Top 11%
Ovarian cancer diagnosis and treatment
1.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.