Evaluation of ovarian cancer prognosis by nomogram model based on prospective cohort study

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.