Journal
FRONTIERS IN ONCOLOGY
Volume 12, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2022.918780
Keywords
nomogram; old age; prostate cancer; CSS; SEER
Categories
Funding
- Special Key Project of Chongqing Technology Innovation and Application Development [Cstc2019jscx-tjsbX0003]
- Yunnan Education Department of Science Research Fund [2020 J0228]
- Kunming City Health Science and Technology Talent 1000 training Project [2020-SW (Reserve)-112]
- Kunming Health and Health Commission Health Research Project [2020-0201-001]
- Kunming Medical Joint Project of Yunnan Science and Technology Department [202001 AY070001-271]
Ask authors/readers for more resources
The study aimed to construct nomograms for predicting the survival of elderly prostate cancer patients. Data of patients aged 65 years and older were obtained from a large cancer database. Independent risk factors were identified, and nomograms were developed based on regression analysis. The nomograms showed good accuracy, discriminability, and clinical value.
ObjectiveProstate 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. MethodsInformation 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. Results135183 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. ConclusionsWe 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available