Gleason score, surgical and distant metastasis are associated with cancer-specific survival and overall survival in middle aged high-risk prostate cancer: A population-based study - Cao, Guangbiao; Li, Yan; Wang, Jinkui; Wu, Xin; Zhang, Zhaoxia; Zhanghuang, Chenghao; Han, Keqiang (Frontiers in Public Health, 2022)
Objective: Patients with high-risk prostate cancer 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. We aimed to construct new nomograms for predicting cancer-specific survival (CSS) and Overall survival (OS) in middle-aged, high-risk PC patients. Methods: Data for patients aged between 50 and 65 years old and diagnosed with no 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 consistency index (C-index), the area under the subject 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. Results: Between 2010 and 2018, 1,651 patients diagnosed with high-risk PC and aged 50 to 65 years were included in this study. In this study, the training group (n=1146) and the validation group (n=505) were randomly assigned in a ratio of 7:3. The results showed that M stage, GS and surgical mode were independent risk factors for CSS; marital status, T stage, M stage, surgical pattern and GS were independent risk factors for OS. The c-index for predicting CSS in the training and validation sets are 0.84 and 0.811, respectively, the C-index for predicing OS in the training and validation sets are 0.824 and 0.784, respectively. The nomogram of both CSS and OS showed good discriminatory power. The AUC and the calibration curves also showed good accuracy and discriminability. Conclusions: We constructed new nomograms to predict CSS and OS in middle-aged high-risk PC patients. The predictive model, after internal cross-validation, shows good accuracy and reliability, as well as potential clinical value, which can help clinicians and patients to make better clinical aid decisions.