Prognostic significance of surgery and radiotherapy in elderly patients with localized prostate cancer:A SEER-based study

Abstract

Abstract Objective Prostate cancer (PC) is a significant disease affecting men's health worldwide. More than 60% of patients over 65 years old and more than 80% are diagnosed with localized PC. The current choice of treatment modalities for localized PC and whether overtreatment is controversial. Therefore, we wanted to construct a nomogram to predict the risk factors associated with cancer-specific survival (CSS) and overall survival (OS) in elderly patients with localized PC while assessing the survival differences in surgery and radiotherapy for elderly patients with localized PC. Methods The information of patient was obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and this study was aimed at localized PC patients over 65 years. Independent risk factors for assessing CSS and OS of patients were determined by univariate and multivariate Cox regression models. A multivariate Cox regression model was used to establish nomograms for predicting CSS and OS. The accuracy and discriminability of the prediction model were tested by the concordance index (C-index), the area under the receiver operating characteristic curve (AUC) and the calibration curve. Decision curve analysis (DCA) was used to test the potential clinical value of this model. Results From 2010 to 2018, there were a total of 90,434 PC patients included in this study, all of whom were diagnosed with localized PC and were over 65 years of age. This study is divided into training set (n = 63328) and validation set (n = 27106) according to the ratio of 7:3. The results showed that independent risk factors for predicting CSS in elderly localized PC patients included T stage, age, surgery, marriage, radiotherapy, prostate-specific antigen (PSA), Gleason score (GS), and race. The independent risk factors for predicting OS included surgery, radiotherapy, marriage, age, race, GS and PSA. The c-index of the training and validation sets for predicting OS is 0.712(95%:0.704–0.720) and 0.724(95%:0.714–0.734). It shows that the nomograms have excellent discriminatory ability. The AUC and the calibration curves also show good accuracy and discriminability. Conclusions We have developed new nomograms to predict CSS and OS in elderly patients with localized PC. After internal validation and external temporal validation with good accuracy and reliability, and potential clinical value, the model can be used for clinically assisted decision-making.