Nomogram model for predicting the long-term prognosis of cervical cancer patients: a population-based study in Mato Grosso, Brazil

Abstract Background Cervical cancer (CC) is the third most common cancer among women worldwide and the second most prevalent neoplasm in Mato Grosso, Brazil, in 2020. This study aimed to analyze overall survival (OS), identify prognostic factors, and develop a nomogram to predict the long-term prognosis of CC patients using population-based data from Mato Grosso, Brazil. Methods Integrated data from the Mortality Information System (SIM) and the Population-Based Cancer Registry (RCBP) were used for patients diagnosed with CC between 2001 and 2018. Group differences were analyzed using the Log-rank test, and survival analysis was performed using the Kaplan-Meier method. Univariable and multivariable Cox regression models were applied to identify predictors of OS. A nomogram was developed to predict OS at 1, 3, 5, and 10 years. The accuracy of the model was assessed using the C-index, receiver operating characteristic (ROC) curve, and calibration plots. Results The median follow-up time was 12 years (range: 6.28 − 17.1). The OS rates at 1, 3, 5, and 10 years were 95.4%, 91.3%, 89.9%, and 88.3%, respectively. Age, histological type, and disease stage were identified as independent prognostic factors for OS. The C-index for OS was 0.869, and the areas under the ROC curve for 1, 3, 5, and 10 years were 0.910, 0.897, 0.895, and 0.884, respectively, indicating good discrimination. The nomogram demonstrated good agreement with the observed survival rates. Conclusion The developed nomogram predicts OS for CC patients at 1, 3, 5, and 10 years, showing good concordance with the observed survival rates and serving as a useful tool for guiding personalized interventions. Notably, disease staging and histopathological type were the most significant prognostic factors for OS.