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000166492 0247_ $$2doi$$a10.1158/1055-9965.EPI-20-1438
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000166492 1001_ $$aSingleton, Rosie K$$b0
000166492 245__ $$aRisk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study.
000166492 260__ $$aPhiladelphia, Pa.$$bAACR$$c2021
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000166492 500__ $$a2021 Mar;30(3):507-512
000166492 520__ $$aEarly detection of renal cell carcinoma (RCC) has the potential to improve disease outcomes. No screening programme 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 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.
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000166492 7001_ $$00000-0001-6517-1300$$aHeath, Alicia K$$b1
000166492 7001_ $$00000-0003-4231-5740$$aClasen, Joanna L$$b2
000166492 7001_ $$aScelo, Ghislaine$$b3
000166492 7001_ $$aJohannson, Mattias$$b4
000166492 7001_ $$aLe Calvez-Kelm, Florence$$b5
000166492 7001_ $$00000-0003-2237-0128$$aWeiderpass, Elisabete$$b6
000166492 7001_ $$aLiedberg, Fredrik$$b7
000166492 7001_ $$00000-0002-4121-3753$$aLjungberg, Borje$$b8
000166492 7001_ $$aHarbs, Justin$$b9
000166492 7001_ $$aOlsen, Anja$$b10
000166492 7001_ $$00000-0003-4385-2097$$aTjonneland, Anne$$b11
000166492 7001_ $$00000-0003-0481-2893$$aDahm, Christina C$$b12
000166492 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b13$$udkfz
000166492 7001_ $$0P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2$$aTurzanski-Fortner, Renée$$b14$$udkfz
000166492 7001_ $$aPanico, Salvatore$$b15
000166492 7001_ $$00000-0001-8165-5524$$aTagliabue, Giovanna$$b16
000166492 7001_ $$00000-0002-5758-9069$$aMasala, Giovanna$$b17
000166492 7001_ $$00000-0003-2666-414X$$aTumino, Rosario$$b18
000166492 7001_ $$00000-0001-8749-9737$$aRicceri, Fulvio$$b19
000166492 7001_ $$00000-0002-0031-4152$$aGram, Inger T$$b20
000166492 7001_ $$aSantiuste, Carmen$$b21
000166492 7001_ $$aBonet, Catalina$$b22
000166492 7001_ $$00000-0002-9972-9779$$aRodríguez-Barranco, Miguel$$b23
000166492 7001_ $$00000-0002-0830-5277$$aSchulze, Matthias B$$b24
000166492 7001_ $$00000-0001-5064-227X$$aBergmann, Manuela M$$b25
000166492 7001_ $$00000-0002-9571-0763$$aTravis, Ruth C$$b26
000166492 7001_ $$aTzoulaki, Ioanna$$b27
000166492 7001_ $$aRiboli, Elio$$b28
000166492 7001_ $$aMuller, David C$$b29
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