Risk Prediction for Renal Cell Carcinoma: Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Prospective Cohort Study
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Risk Prediction for Renal Cell Carcinoma : Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Prospective Cohort Study. / Singleton, Rosie K.; Heath, Alicia K.; Clasen, Joanna L.; Scelo, Ghislaine; Johansson, Mattias; Le Calvez-Kelm, Florence; Weiderpass, Elisabete; Liedberg, Fredrik; Ljungberg, Borje; Harbs, Justin; Olsen, Anja; Tjonneland, Anne; Dahm, Christina C.; Kaaks, Rudolf; Fortner, Renee T.; Panico, Salvatore; Tagliabue, Giovanna; Masala, Giovanna; Tumino, Rosario; Ricceri, Fulvio; Gram, Inger T.; Santiuste, Carmen; Bonet, Catalina; Rodriguez-Barranco, Miguel; Schulze, Mattias B.; Bergmann, Manuela M.; Travis, Ruth C.; Tzoulaki, Ioanna; Riboli, Elio; Muller, David C.
In: Cancer Epidemiology, Biomarkers & Prevention, Vol. 30, No. 3, 2021, p. 507-512.Research output: Contribution to journal › Journal article › peer-review
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TY - JOUR
T1 - Risk Prediction for Renal Cell Carcinoma
T2 - Results from the European Prospective Investigation into Cancer and Nutrition (EPIC) Prospective Cohort Study
AU - Singleton, Rosie K.
AU - Heath, Alicia K.
AU - Clasen, Joanna L.
AU - Scelo, Ghislaine
AU - Johansson, Mattias
AU - Le Calvez-Kelm, Florence
AU - Weiderpass, Elisabete
AU - Liedberg, Fredrik
AU - Ljungberg, Borje
AU - Harbs, Justin
AU - Olsen, Anja
AU - Tjonneland, Anne
AU - Dahm, Christina C.
AU - Kaaks, Rudolf
AU - Fortner, Renee T.
AU - Panico, Salvatore
AU - Tagliabue, Giovanna
AU - Masala, Giovanna
AU - Tumino, Rosario
AU - Ricceri, Fulvio
AU - Gram, Inger T.
AU - Santiuste, Carmen
AU - Bonet, Catalina
AU - Rodriguez-Barranco, Miguel
AU - Schulze, Mattias B.
AU - Bergmann, Manuela M.
AU - Travis, Ruth C.
AU - Tzoulaki, Ioanna
AU - Riboli, Elio
AU - Muller, David C.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - CARDIOVASCULAR RISK
KW - KIDNEY CANCER
KW - BODY-SIZE
KW - HYPERTENSION
KW - OBESITY
KW - BLADDER
KW - MODELS
U2 - 10.1158/1055-9965.EPI-20-1438
DO - 10.1158/1055-9965.EPI-20-1438
M3 - Journal article
C2 - 33335022
VL - 30
SP - 507
EP - 512
JO - Cancer Epidemiology, Biomarkers & Prevention
JF - Cancer Epidemiology, Biomarkers & Prevention
SN - 1055-9965
IS - 3
ER -
ID: 259425452