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; Johannson, Mattias; Le Calvez-Kelm, Florence; Weiderpass, Elisabete; Liedberg, Fredrik; Ljungberg, Borje; Harbs, Justin; Olsen, Anja; Tjonneland, Anne; Dahm, Christina C; Kaaks, Rudolf; Fortner, Renée Turzanski; Panico, Salvatore; Tagliabue, Giovanna; Masala, Giovanna; Tumino, Rosario; Ricceri, Fulvio; Gram, Inger T; Santiuste, Carmen; Bonet, Catalina; Rodríguez-Barranco, Miguel; Schulze, Matthias B; Bergmann, Manuela M; Travis, Ruth C; Tzoulaki, Ioanna; Riboli, Elio; Muller, David C.

In: Cancer Epidemiology, Biomarkers & Prevention, Vol. 30, No. 3, 03.2021, p. 507-512.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Harvard

Singleton, RK, Heath, AK, Clasen, JL, Scelo, G, Johannson, M, Le Calvez-Kelm, F, Weiderpass, E, Liedberg, F, Ljungberg, B, Harbs, J, Olsen, A, Tjonneland, A, Dahm, CC, Kaaks, R, Fortner, RT, Panico, S, Tagliabue, G, Masala, G, Tumino, R, Ricceri, F, Gram, IT, Santiuste, C, Bonet, C, Rodríguez-Barranco, M, Schulze, MB, Bergmann, MM, Travis, RC, Tzoulaki, I, Riboli, E & Muller, DC 2021, 'Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study', Cancer Epidemiology, Biomarkers & Prevention, vol. 30, no. 3, pp. 507-512. https://doi.org/10.1158/1055-9965.EPI-20-1438

APA

Singleton, R. K., Heath, A. K., Clasen, J. L., Scelo, G., Johannson, M., Le Calvez-Kelm, F., Weiderpass, E., Liedberg, F., Ljungberg, B., Harbs, J., Olsen, A., Tjonneland, A., Dahm, C. C., Kaaks, R., Fortner, R. T., Panico, S., Tagliabue, G., Masala, G., Tumino, R., ... Muller, D. C. (2021). Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study. Cancer Epidemiology, Biomarkers & Prevention, 30(3), 507-512. https://doi.org/10.1158/1055-9965.EPI-20-1438

CBE

Singleton RK, Heath AK, Clasen JL, Scelo G, Johannson M, Le Calvez-Kelm F, Weiderpass E, Liedberg F, Ljungberg B, Harbs J, Olsen A, Tjonneland A, Dahm CC, Kaaks R, Fortner RT, Panico S, Tagliabue G, Masala G, Tumino R, Ricceri F, Gram IT, Santiuste C, Bonet C, Rodríguez-Barranco M, Schulze MB, Bergmann MM, Travis RC, Tzoulaki I, Riboli E, Muller DC. 2021. Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study. Cancer Epidemiology, Biomarkers & Prevention. 30(3):507-512. https://doi.org/10.1158/1055-9965.EPI-20-1438

MLA

Vancouver

Singleton RK, Heath AK, Clasen JL, Scelo G, Johannson M, Le Calvez-Kelm F et al. Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study. Cancer Epidemiology, Biomarkers & Prevention. 2021 Mar;30(3):507-512. https://doi.org/10.1158/1055-9965.EPI-20-1438

Author

Singleton, Rosie K ; Heath, Alicia K ; Clasen, Joanna L ; Scelo, Ghislaine ; Johannson, Mattias ; Le Calvez-Kelm, Florence ; Weiderpass, Elisabete ; Liedberg, Fredrik ; Ljungberg, Borje ; Harbs, Justin ; Olsen, Anja ; Tjonneland, Anne ; Dahm, Christina C ; Kaaks, Rudolf ; Fortner, Renée Turzanski ; Panico, Salvatore ; Tagliabue, Giovanna ; Masala, Giovanna ; Tumino, Rosario ; Ricceri, Fulvio ; Gram, Inger T ; Santiuste, Carmen ; Bonet, Catalina ; Rodríguez-Barranco, Miguel ; Schulze, Matthias B ; Bergmann, Manuela M ; Travis, Ruth C ; Tzoulaki, Ioanna ; Riboli, Elio ; Muller, David C. / Risk prediction for renal cell carcinoma : results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study. In: Cancer Epidemiology, Biomarkers & Prevention. 2021 ; Vol. 30, No. 3. pp. 507-512.

Bibtex

@article{68a4be6a6a7f4946b7363f2de71236d4,
title = "Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study",
abstract = "BACKGROUND: Early 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.METHODS: 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.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.",
author = "Singleton, {Rosie K} and Heath, {Alicia K} and Clasen, {Joanna L} and Ghislaine Scelo and Mattias Johannson and {Le Calvez-Kelm}, Florence and Elisabete Weiderpass and Fredrik Liedberg and Borje Ljungberg and Justin Harbs and Anja Olsen and Anne Tjonneland and Dahm, {Christina C} and Rudolf Kaaks and Fortner, {Ren{\'e}e Turzanski} and Salvatore Panico and Giovanna Tagliabue and Giovanna Masala and Rosario Tumino and Fulvio Ricceri and Gram, {Inger T} and Carmen Santiuste and Catalina Bonet and Miguel Rodr{\'i}guez-Barranco and Schulze, {Matthias B} and Bergmann, {Manuela M} and Travis, {Ruth C} and Ioanna Tzoulaki and Elio Riboli and Muller, {David C}",
note = "Copyright {\textcopyright}2020, American Association for Cancer Research.",
year = "2021",
month = mar,
doi = "10.1158/1055-9965.EPI-20-1438",
language = "English",
volume = "30",
pages = "507--512",
journal = "Cancer Epidemiology, Biomarkers & Prevention",
issn = "1055-9965",
publisher = "American Association for Cancer Research (A A C R)",
number = "3",

}

RIS

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 - Johannson, 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, Renée Turzanski

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 - Rodríguez-Barranco, Miguel

AU - Schulze, Matthias B

AU - Bergmann, Manuela M

AU - Travis, Ruth C

AU - Tzoulaki, Ioanna

AU - Riboli, Elio

AU - Muller, David C

N1 - Copyright ©2020, American Association for Cancer Research.

PY - 2021/3

Y1 - 2021/3

N2 - BACKGROUND: Early 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.METHODS: 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.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 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.METHODS: 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.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.

UR - http://www.scopus.com/inward/record.url?scp=85102127616&partnerID=8YFLogxK

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 -