Prognostic nomograms for locally advanced cervical cancer based on the SEER database: Integrating Cox regression and competing risk analysis

Abstract

Locally advanced cervical carcinoma (LACC) remains a significant global health challenge owing to its high recurrence rates and poor outcomes, despite current treatments. This study aimed to develop a comprehensive risk stratification model for LACC by integrating Cox regression and competing risk analyses. This was done to improve clinical decision making. We analyzed data from 3428 patients with LACC registered in the Surveillance, Epidemiology, and End Results program and diagnosed them between 2010 and 2015. Cox regression and competing risk analyses were used to identify the prognostic factors. We constructed and validated nomograms for overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression identified key prognostic factors for OS, including advanced International Federation of Gynecology and Obstetrics stage, age, marital status, ethnicity, and tumor size. Notably, International Federation of Gynecology and Obstetrics stages IIIA, IIIB, and IVA had hazard ratios of 2.227, 2.451, and 4.852, respectively, significantly increasing the mortality risk compared to stage IB2. Ethnic disparities were evident, with African Americans facing a 39.8% higher risk than Caucasians did. Competing risk analyses confirmed the significance of these factors in DSS, particularly tumor size. Our nomogram demonstrated high predictive accuracy, with area under the curve values ranging from 0.706 to 0.784 for DSS and 0.717 to 0.781 for OS. Calibration plots and decision curve analyses further validated the clinical utility of this nomogram. We present effective nomograms for LACC risk stratification that incorporate multiple prognostic factors. These models provide a refined approach for individualized patient management and have the potential to significantly enhance therapeutic strategies for LACC.

Figure 1.

Trends in the annual age-adjusted incidence of LACC from 2010 to 2015. The vertical axis represents the number of cases per 100,000 individuals, while the horizontal axis shows the year of diagnosis. Data points are marked in red, depicting a “U-shaped” pattern over the 6-year timeframe. The incidence rate per 100,000 was 0.27 in 2010, dropped to 0.25 in 2012, and then steadily increased to 0.29 by 2015. The gray area in the graph indicates the confidence interval for the incidence rates, providing a visual representation of the range of data fluctuations. LACC = locally advanced cervical cancer.

Figure 2.

Optimal cutoff values for age and tumor size in LACC patients. (A) The survival curve for age and (B) for tumor size. Both curves demonstrate statistically significant differences, with P-values <.0001. Survival probability decreases over time, with lower survival rates observed in the older age group and the larger tumor size group. The “Strata” labels indicate different risk groups, with blue representing the low-risk group (younger age/smaller tumor) and red indicating the high-risk group (older age/larger tumor). The table below each point in time displays the number of patients at risk at that time. LACC = locally advanced cervical carcinoma.

Figure 3.

Prognostic evaluation of OS in LACC using nomograms and risk stratification. (A) A detailed nomogram that estimates the probability of 1-, 3-, 5-, and 7-year OS based on a range of clinical and demographic variables. Points are assigned to age, tumor size, FIGO stage, histological grade, marital status, race, type of radiation therapy, surgical intervention, and sequence of treatment. This results in a total score that corresponds to a risk stratification category and survival probability. (B) Kaplan–Meier survival curves stratified into high, intermediate, and low-risk groups based on the total points calculated from the nomogram. The curves provide a visual representation of survival probabilities over time for each risk group. A log-rank test confirms the statistical significance of differences observed. The number of patients at risk at various time points is also indicated, providing context for the survival probabilities displayed. The low-risk group shows high survival resilience, with a 1-year survival rate of 96.9% and a 7-year rate of 75.0%. Intermediate-risk patients have a 1-year survival rate of 91.0%, which decreases to 54.5% at 7 years. The high-risk group starts at a 1-year survival rate of 71.6%, dropping dramatically to 24.8% at 7 years, demonstrating the most significant decline. OS = overall survival; FIGO = International Federation of Gynecology and Obstetrics; LACC = locally advanced cervical carcinoma.

Figure 4.

Prognostic nomogram and survival curves for DSS in LACC. (A) A nomogram that assigns points to clinical variables such as FIGO stage, histological grade, race, type of radiation therapy, surgery sequence, and tumor size. This culminates in a total score that indicates a patient’s risk category and corresponding survival probabilities at 1, 3, 5, and 7 years. (B) Kaplan–Meier survival curves segmented into high, medium, and low-risk groups as determined by the nomogram’s scoring system. These curves visually represent the differences in survival rates over time among the risk categories, with statistical validation provided by the log-rank test. The number at risk at various time intervals is also noted, offering a detailed view of survival trends according to stratified risk groups. The low-risk cohort maintains excellent survival rates, starting at 96.5% at 1 year and declining to 78.0% at 7 years. The intermediate-risk cohort experienced a more pronounced decrease from 89.0% at 1 year to 56.4% at 7 years. The high-risk category showed the steepest decline, from 75.1% at 1 year to 35.6% at 7 years, highlighting the severity of risk associated with this group. DSS = disease-specific survival; FIGO = International Federation of Gynecology and Obstetrics; LACC = locally advanced cervical carcinoma.