ArticleInternational journal of women's health2025
Development and Validation of a Nomogram for Prognostic Prediction in Ovarian Cancer Based on Tumor Proliferation and Immune-Related Biomarkers in Chinese Patients.
Article in International journal of women's health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To develop a nomogram prediction model for ovarian cancer prognosis using tumor proliferation and immune-related biomarkers. Methods: Between January 2018 and December 2023, clinical data were collected from 140 patients diagnosed with epithelial ovarian cancer (EOC). These patients were randomly allocated into a training cohort consisting of 84 patients and a validation cohort comprising 56 patients, adhering to a 6:4 ratio. Immunohistochemical staining assessed Ki67, epidermal growth factor receptor (EGFR), and programmed death-ligand 1 (PD-L1) expression. Lasso-Cox regression identified variables for the nomogram model. Model performance was evaluated using time-dependent receiver operating characteristic (ROC) curves, concordance index, calibration curves, and decision curve analysis. Kaplan-Meier survival analysis assessed the prognostic value of the model's risk score. Results: Lasso-Cox regression identified seven variables for constructing the nomogram prediction model: maximum tumor diameter, KI67 positive rate, pathological grade, N stage, M stage, KI67 ≥ 20%, and PD-L1 > 0. The area under the ROC curve (AUC) for 1-, 4-, and 6-year overall survival (OS) in the training set were 0.908, 0.940, and 0.965, respectively, with a concordance index of 0.85 [95% confidence interval (CI): 0.80-0.90]. In the validation set, the AUCs for 1-, 4-, and 6-year OS were 0.835, 0.802, and 0.832, respectively, with a concordance index of 0.72 (95% CI: 0.65-0.79). Calibration curves demonstrated good agreement between predicted and actual outcomes (as indicated by non-significant Hosmer-Lemeshow tests, all P>0.05). The model outperformed TNM staging in clinical benefit. High-risk scores correlated with poorer overall and progression-free survival (P<0.01). These findings suggest the nomogram can effectively stratify patients and predict prognosis. Conclusion: The successful development and validation of a nomogram prediction model based on tumor proliferation and immune-related biomarkers offers an efficient and straightforward clinical tool. This tool holds promise for enabling personalized treatment strategies for patients with ovarian cancer.
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