Evaluation of ovarian cancer prognosis by nomogram model based on prospective cohort study
Abstract
Objective
Ovarian cancer (OC), accounting for 3.4% of female cancer diagnoses and 4.8% of cancer-related deaths globally, faces high recurrence risks. We aimed to develop a nomogram integrating novel biomarkers to improve prognostic accuracy for OC patients.
Methods
Clinical data from 1342 OC patients at Chongqing University Cancer Hospital (2019–21) were analyzed. Multivariate Cox regression identified independent prognostic factors to construct the nomogram. Model performance was evaluated via the C-index, time-dependent area under the receiver operating characteristic curve, calibration curves, and decision curve analysis (DCA).
Results
The independent prognostic factors for OC in this study include the body mass index, International Federation of Gynecology and Obstetrics stage, differentiation, surgery, targeted therapy, hemoglobin, β2 microglobulin, neutrophil-to-lymphocyte ratio, interleukin-6, and keratin 19. In both the training and validation cohorts, the C-indexes were 0.756 (95% CI: 0.718–0.793) and 0.751 (95% CI: 0.697–0.805), respectively. The calibration curve demonstrated a high level of consistency between the predicted and observed probabilities. DCA confirmed that the nomogram model provided a higher net benefit.
Conclusions
This study established a prognostic nomogram for OC and validated it with rigorous statistical metrics. An online tool was developed to facilitate personalized treatment strategies, offering clinical utility for OC management.