Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12916-018-1019-5
Title: The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: A validation study
Authors: Gnanapragasam, V.J
Bratt, O
Muir, K
Lee, L.S 
Huang, H.H
Stattin, P
Lophatananon, A
Keywords: adult
aged
Article
cancer classification
cancer mortality
cancer prognosis
cancer radiotherapy
cancer risk
cohort analysis
conservative treatment
groups by age
high risk population
human
intermediate risk population
major clinical study
male
middle aged
outcome assessment
pathology
prediction
primary tumor
prostate cancer
prostatectomy
Singapore
validation process
mortality
prognosis
prostate tumor
survival rate
trends
Cohort Studies
Humans
Male
Mortality
Prognosis
Prostatic Neoplasms
Survival Rate
Issue Date: 2018
Citation: Gnanapragasam, V.J, Bratt, O, Muir, K, Lee, L.S, Huang, H.H, Stattin, P, Lophatananon, A (2018). The Cambridge Prognostic Groups for improved prediction of disease mortality at diagnosis in primary non-metastatic prostate cancer: A validation study. BMC Medicine 16 (1) : 31. ScholarBank@NUS Repository. https://doi.org/10.1186/s12916-018-1019-5
Rights: Attribution 4.0 International
Abstract: Background: The purpose of this study is to validate a new five-tiered prognostic classification system to better discriminate cancer-specific mortality in men diagnosed with primary non-metastatic prostate cancer. Methods: We applied a recently described five-strata model, the Cambridge Prognostic Groups (CPGs 1-5), in two international cohorts and tested prognostic performance against the current standard three-strata classification of low-, intermediate- or high-risk disease. Diagnostic clinico-pathological data for men obtained from the Prostate Cancer data Base Sweden (PCBaSe) and the Singapore Health Study were used. The main outcome measure was prostate cancer mortality (PCM) stratified by age group and treatment modality. Results: The PCBaSe cohort included 72,337 men, of whom 7162 died of prostate cancer. The CPG model successfully classified men with different risks of PCM with competing risk regression confirming significant intergroup distinction (p < 0.0001). The CPGs were significantly better at stratified prediction of PCM compared to the current three-tiered system (concordance index (C-index) 0.81 vs. 0.77, p < 0.0001). This superiority was maintained for every age group division (p < 0.0001). Also in the ethnically different Singapore cohort of 2550 men with 142 prostate cancer deaths, the CPG model outperformed the three strata categories (C-index 0.79 vs. 0.76, p < 0.0001). The model also retained superior prognostic discrimination in the treatment sub-groups: radical prostatectomy (n = 20,586), C-index 0.77 vs. 074; radiotherapy (n = 11,872), C-index 0.73 vs. 0.69; and conservative management (n = 14,950), C-index 0.74 vs. 0.73. The CPG groups that sub-divided the old intermediate-risk (CPG2 vs. CPG3) and high-risk categories (CPG4 vs. CPG5) significantly discriminated PCM outcomes after radical therapy or conservative management (p < 0.0001). Conclusions: This validation study of nearly 75,000 men confirms that the CPG five-tiered prognostic model has superior discrimination compared to the three-tiered model in predicting prostate cancer death across different age and treatment groups. Crucially, it identifies distinct sub-groups of men within the old intermediate-risk and high-risk criteria who have very different prognostic outcomes. We therefore propose adoption of the CPG model as a simple-to-use but more accurate prognostic stratification tool to help guide management for men with newly diagnosed prostate cancer. © 2018 The Author(s).
Source Title: BMC Medicine
URI: https://scholarbank.nus.edu.sg/handle/10635/178104
ISSN: 17417015
DOI: 10.1186/s12916-018-1019-5
Rights: Attribution 4.0 International
Appears in Collections:Elements
Staff Publications

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
10_1186_s12916-018-1019-5.pdf969.62 kBAdobe PDF

OPEN

NoneView/Download

Google ScholarTM

Check

Altmetric


This item is licensed under a Creative Commons License Creative Commons