According to statistics, patients with high-risk prostate cancer (PC) account for about 15% of prostate cancer diagnoses, and high-risk patients usually have a poor prognosis due to metastasis and recurrence and have a high mortality rate. Therefore, the accurate prediction of prognostic-related risk factors in middle-aged high-risk PC patients between 50 and 65 can help reduce patient mortality. We aimed to construct new nomograms for predicting cancer-specific survival (CSS) and Overall survival (OS) in middle-aged high-risk PC patients.
Data for patients aged between 50 and 65 years old and diagnosed with high-risk PC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for CSS and OS in patients. Nomograms predicting CSS and OS were developed based on multivariate Cox regression models. The concordance index (C-index), the area under the receiver operating characteristic curve (AUC), and the calibration curve are used to detect the accuracy and discrimination of the model. Decision curve analysis (DCA) is used to detect the potential clinical value of this model.
Between 2010 and 2018, 1,651 patients diagnosed with high-risk PC and aged 50–65 years were included. In this study, the training group (
We constructed new nomograms to predict CSS and OS in middle-aged high-risk PC patients. The prediction tools showed good accuracy and reliability, which can help clinicians and patients to make better clinical decisions.