Construction and validation of nomograms for predicting overall survival and cause-specific survival in cervical cancer patients undergoing radical radiotherapy based on the SEER database.

Objective: This study aimed to construct and validate competitive-risk model nomograms using the Surveillance, Epidemiology, and End Results (SEER) database to predict the overall survival (OS) and cause-specific survival (CSS) for cervical cancer patients treated with radical radiotherapy from 2000 to 2020. Materials: Cervical cancer cases treated with radical radiotherapy in the SEER database were retrieved. Inclusion criteria included the pathological diagnosis of cervical cancer, diagnosis within the specified time frame, and age between 20 and 79 years. Exclusion criteria were the presence of other malignant tumors, cancer-directed surgery, and incomplete data. Variables such as age, diagnosis year, and race were extracted. Patients were randomly divided into a training set and a validation set at a 4:1 ratio using a stratified random sampling method. For the training set patients, univariate and multivariate Cox regression analyses were performed, and CRM nomograms to predict the 3-year and 5-year OS and CSS were created in R. Harrell's concordance index (C-index) and a calibration curve were used to assess model performance. Results: A total of 8,810 patients were included for OS and CSS analysis. The median follow-up was 26 months. The median OS was 64.0 months and CSS was 135.0 months. Diagnosis year, marital status, histologic type, chemotherapy, T stage, N stage, M stage, tumor size, median household income, and radiation modality were factors influencing the median survival time of OS and CSS. Age was an independent factor influencing OS. The C-index for OS and CSS predictions were 0.72 [95% confidence intervals (CI), 0.70 to 0.74] and 0.73 (95% CI, 0.71 to 0.75), respectively. Calibration plots showed good agreement between nomogram predictions and actual observations. Conclusion: The nomograms can objectively and precisely predict the OS and CSS of cervical cancer patients receiving radical radiotherapy.