Clinical prediction model for patients with early-onset prostate cancer without surgical treatment: Based on the SEER Database.
- Author:
Han-Dong LIU
1
;
Han-Yu JIA
1
;
Jing WANG
2
;
Li-Ping ZHANG
1
Author Information
1. School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China.
2. Institute for Endemic and Parasitic Disease Control, Qingdao Center for Disease Control and Prevention, Qingdao, Shandong 266033, China.
- Publication Type:Journal Article
- Keywords:
surveillance, epidemiology, and end results;
early-onset prostate cancer;
nomogram;
prediction model
- MeSH:
Humans;
Male;
Prostatic Neoplasms/diagnosis*;
Middle Aged;
Nomograms;
SEER Program;
Prognosis;
Adult;
Prostate-Specific Antigen;
Risk Factors;
Proportional Hazards Models;
Neoplasm Grading;
ROC Curve
- From:
National Journal of Andrology
2025;31(5):412-420
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:The aim of this study is to investigate the risk factors of prognosis in patients with early-onset prostate cancer treated without surgery. A nomogram will be constructed and validated to predict overall survival (OS) of patients with early-onset prostate cancer treated without surgery.
METHODS:The clinical data was obtained from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database on prostate cancer patients aged 18－55 years who were treated without surgery between 2010 and 2015. The clinical data set was divided into training set and validation set according to 7∶3 ratio, including age, race, marital status, Gleason score, prostate specific antigen (PSA) and other 8 factors. And significant variables were screened by univariate Cox regression analysis. Multivariate Cox regression analysis was used to identify the influence factors. Stepwise regression method was used to select the most influential factors on the total OS, and R software was used to build a nomogram model. The accuracy and prediction ability of the model were verified by drawing receiver operating characteristic (ROC) and Calibration Plot. The clinical benefit of the model was evaluated by decision curve analysis (DCA).
RESULTS:A total of 8 212 patients who met the criteria were randomly assigned to the training set (n=5 752) or validation set (n=2 460), with no statistical difference between the two groups (all P>0.05). Six factors were identified through univariate and multivariate Cox regression analysis including marital status, N stage, M stage, radiotherapy, PSA and Gleason score, which were most closely associated with the OS of prostate cancer patients, and a column graph model was constructed based on these factors. The Consistency index (C-index) of the model in the training set and the verification set were 0.802 and 0.794, respectively. And the apparent diffusion coefficient (AUC) was 0.851, 0.855 and 0.855 for training sets 1, 3 and 5 years, and 0.694, 0.860 and 0.832 for verification sets 1, 3 and 5 years. The calibration chart showed a good agreement between the predicted and actual values of the model. In the analysis of decision curve, the model showed good clinical application value.
CONCLUSION:The prediction model based on marital status, radiotherapy, M stage, N stage, PSA and Gleason score for early-onset prostate cancer patients without surgical treatment has certain reference value which is expected to become an effective tool for clinicians to treat in future prospective studies on large and multi-center samples.
