Risk Prediction for Renal Cell Carcinoma: Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Prospective Cohort Study.
Journal
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
ISSN: 1538-7755
Titre abrégé: Cancer Epidemiol Biomarkers Prev
Pays: United States
ID NLM: 9200608
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
02
10
2020
revised:
11
11
2020
accepted:
14
12
2020
pubmed:
19
12
2020
medline:
3
2
2022
entrez:
18
12
2020
Statut:
ppublish
Résumé
Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives. Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure. The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679-0.721]). Our model had slightly improved discrimination (0.714 [0.694-0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025. Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population. Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.
Sections du résumé
BACKGROUND
Early detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening program for sporadic RCC is in place. Given relatively low incidence, screening would need to focus on people at high risk of clinically meaningful disease so as to limit overdiagnosis and screen-detected false positives.
METHODS
Among 192,172 participants from the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort (including 588 incident RCC cases), we evaluated a published RCC risk prediction model (including age, sex, BMI, and smoking status) in terms of discrimination (C-statistic) and calibration (observed probability as a function of predicted probability). We used a flexible parametric survival model to develop an expanded model including age, sex, BMI, and smoking status, with the addition of self-reported history of hypertension and measured blood pressure.
RESULTS
The previously published model yielded well-calibrated probabilities and good discrimination (C-statistic [95% CI]: 0.699 [0.679-0.721]). Our model had slightly improved discrimination (0.714 [0.694-0.735], bootstrap optimism-corrected C-statistic: 0.709). Despite this good performance, predicted risk was low for the vast majority of participants, with 70% of participants having 10-year risk less than 0.0025.
CONCLUSIONS
Although the models performed well for the prediction of incident RCC, they are currently insufficiently powerful to identify individuals at substantial risk of RCC in a general population.
IMPACT
Despite the promising performance of the EPIC RCC risk prediction model, further development of the model, possibly including biomarkers of risk, is required to enable risk stratification of RCC.
Identifiants
pubmed: 33335022
pii: 1055-9965.EPI-20-1438
doi: 10.1158/1055-9965.EPI-20-1438
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
507-512Subventions
Organisme : Cancer Research UK
ID : C8221/A19170
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 14136
Pays : United Kingdom
Organisme : Cancer Research UK
ID : 24390
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N003284/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : 1000143
Pays : United Kingdom
Organisme : Medical Research Council
ID : G0401527
Pays : United Kingdom
Organisme : Cancer Research UK
ID : C570/A16491
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/M012190/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : G1000143
Pays : United Kingdom
Informations de copyright
©2020 American Association for Cancer Research.
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