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. 2022 Jun 23:12:918780.
doi: 10.3389/fonc.2022.918780. eCollection 2022.

Development and Validation of Nomograms to Predict Cancer-Specific Survival and Overall Survival in Elderly Patients With Prostate Cancer: A Population-Based Study

Zhaoxia Zhang  1   2   3 , Chenghao Zhanghuang  1   2   3   4 , Jinkui Wang  1   2   3 , Xiaomao Tian  1   2   3 , Xin Wu  1   2   3 , Maoxian Li  1   2   3 , Tao Mi  1   2   3 , Jiayan Liu  1   2   3 , Liming Jin  1   2   3 , Mujie Li  1   2   3 , Dawei He  1   2   3
Affiliations

Development and Validation of Nomograms to Predict Cancer-Specific Survival and Overall Survival in Elderly Patients With Prostate Cancer: A Population-Based Study

Zhaoxia Zhang et al. Front Oncol. .

Abstract

Objective: Prostate cancer (PC) is the most common non-cutaneous malignancy in men worldwide. Accurate predicting the survival of elderly PC patients can help reduce mortality in patients. We aimed to construct nomograms to predict cancer-specific survival (CSS) and overall survival (OS) in elderly PC patients.

Methods: Information on PC patients aged 65 years and older was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to determine independent risk factors for PC patients. Nomograms were developed to predict the CSS and OS of elderly PC patients based on a multivariate Cox regression model. The accuracy and discrimination of the prediction model were tested by the consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve. Decision curve analysis (DCA) was used to test the clinical value of the nomograms compared with the TNM staging system and D'Amico risk stratification system.

Results: 135183 elderly PC patients in 2010-2018 were included. All patients were randomly assigned to the training set (N=94764) and the validation set (N=40419). Univariate and multivariate Cox regression model analysis revealed that age, race, marriage, histological grade, TNM stage, surgery, chemotherapy, radiotherapy, biopsy Gleason score (GS), and prostate-specific antigen (PSA) were independent risk factors for predicting CSS and OS in elderly patients with PC. The C-index of the training set and the validation set for predicting CSS was 0.883(95%CI:0.877-0.889) and 0.887(95%CI:0.877-0.897), respectively. The C-index of the training set and the validation set for predicting OS was 0.77(95%CI:0.766-0.774)and 0.767(95%CI:0.759-0.775), respectively. It showed that the proposed model has excellent discriminative ability. The AUC and the calibration curves also showed good accuracy and discriminability. The DCA showed that the nomograms for CSS and OS have good clinical potential value.

Conclusions: We developed new nomograms to predict CSS and OS in elderly PC patients. The models have been internally validated with good accuracy and reliability and can help doctors and patients to make better clinical decisions.

Keywords: CSS; SEER; nomogram; old age; prostate cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart for inclusion and exclusion of elderly patients with PC.
Figure 2
Figure 2
The nomograms for predicting 3-,5-,8-year CSS and OS in elderly patients with PC. (A) The nomogram for predicting CSS in elderly patients with PC. (B) The nomogram for predicting OS in elderly patients with PC. ***P < 0.001 vs. reference.
Figure 3
Figure 3
Calibration curve of the nomograms for predicting 3-,5-,8-year CSS and OS in elderly patients with PC. (A) Calibration curve of the nomograms for predicting 3-,5-,8-year CSS in the training set. (B) Calibration curve of the nomograms for predicting 3-,5-, and 8-year CSS in the validation set. (C) Calibration curve of the nomograms for predicting 3-,5-,8-year OS in the training set. (D) Calibration curve of the nomograms for predicting 3-,5-,8-year OS in the validation set. The horizontal axis is the predicted value in the nomogram, and the vertical axis is the observed value.
Figure 4
Figure 4
AUC for predicting 3-, 5-, and 8-year CSS and OS in elderly patients with PC. (A) The AUC at 3-, 5-, and 8-year for CSS in the training set was 89.6,87.2, and 85.1. (B) The AUC for CSS in the validation set was 89.9,88.4, and 85.7. (C) The AUC at 3-, 5- and 8-year for OS in the training set was 77.0,75.0, and 75.0. (D) The AUC at 3-, 5- and 8-year for OS in the validation set was 77.4,75.4, and 74.5.
Figure 5
Figure 5
DCA of the nomograms for predicting CSS and OS. (A) The nomogram for CSS at 3,5-year showed the best clinical potential value in the training set, while the nomogram for CSS at 8-year had no apparent advantages over the other two. (B) In the validation set, the nomogram for CSS at the 3,5,8-year showed the best clinical potential value, followed by the D’Amico risk stratification system and TNM staging system. (C, D) The nomogram for OS at 3-,5-,8-year showed the best application potential in both the training and validation sets, followed by D’Amico risk stratification and TNM staging.
Figure 6
Figure 6
Kaplan-Meier curves of patients in the low-risk and high-risk groups. The K-M curve showed that the CSS rate of the patients in the high-risk group was significantly lower than that in the low-risk group both in the training set (A) and validation set (B). The K-M curve showed that the OS rate of the patients in the high-risk group was significantly lower than that in the low-risk group both in the training set (C) and validation set (D).
Figure 7
Figure 7
Kaplan-Meier curves of patients with a different surgery. (A) The CSS rate of patients in the low-risk group underwent different surgery. (B) The CSS rate of patients in the high-risk group underwent different surgery. (C) The OS rate of patients in the low-risk group underwent different surgery. (D) The OS rate of patients in the high-risk group underwent different surgery.
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