Risk prediction for renal cell carcinoma: results from the European Prospective Investigation into Cancer and Nutrition (EPIC) prospective cohort study

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

  • Rosie K Singleton, Imperial College, United Kingdom
  • Alicia K Heath, Imperial College, United Kingdom
  • Joanna L Clasen, Imperial College, United Kingdom
  • Ghislaine Scelo, Unit of Cancer Epidemiology, Department of Medical Sciences, University of Turin, Turin, Italy., Italy
  • Mattias Johannson, WHO International Agency for Research on Cancer, France
  • Florence Le Calvez-Kelm, WHO International Agency for Research on Cancer, France
  • Elisabete Weiderpass, WHO International Agency for Research on Cancer, France
  • Fredrik Liedberg, Institution of Translational Medicine, Sweden
  • Borje Ljungberg, Department of Surgical and Perioperative Sciences, Umeå University, Umeå, Sweden., Sweden
  • Justin Harbs, Umeå University, Department of Radiation Sciences, Sweden
  • Anja Olsen
  • Anne Tjonneland, Diet, Genes and Environment, Danish Cancer Society Research Centre, Department of Public Health, University of Copenhagen, 1123, Copenhagen, Denmark., Denmark
  • Christina C Dahm
  • Rudolf Kaaks, Division of Cancer Epidemiology, German Cancer Research Center, DKFZ, Heidelberg, Germany., Germany
  • Renée Turzanski Fortner, Division of Cancer Epidemiology, German Cancer Research Center, DKFZ, Heidelberg, Germany., Germany
  • Salvatore Panico, Department of Clinical Medicine and Surgery, Unit of Clinical Epidemiology and Predictive Medicine, Federico II University, Italy
  • Giovanna Tagliabue, Lombardy Cancer Registry Unit, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
  • Giovanna Masala, Institute for Cancer Research, Italy
  • Rosario Tumino, Cancer Registry and Histopathology Unit, Azienda Sanitaria Provinciale 7, Ragusa, Italy., Italy
  • Fulvio Ricceri, Department of Clinical and Biological Sciences, University of Turin, Orbassano, Turin, Unit of Epidemiology, Regional Health Service ASL TO3, Grugliasco (TO), Italy., Italy
  • Inger T Gram, University of Tromsø, Tromsø, Norway
  • Carmen Santiuste, Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain., CIBER Epidemiología y Salud Pública (CIBERESP), Spain., Spain
  • Catalina Bonet, Unit of Nutrition, Environment and Cancer, Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Barcelona, Spain., Spain
  • Miguel Rodríguez-Barranco, Biosanitary Investigation Institute (IBS) of Granada, University Hospital and University of Granada, Granada, Spain., Escuela Andaluza de Salud Pública (EASP), Granada, Consorcio Centro de Investigación Biomédica en Red Epidemiología y Salud Pública (CIBERESP), Madrid, Spain., Spain
  • Matthias B Schulze, Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, 14558, Germany., Institute of Nutrition Science, University of Potsdam, Nuthetal, Germany., Germany
  • Manuela M Bergmann, Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbrücke (DIfE), Nuthetal, Germany., Germany
  • Ruth C Travis, Nuffield Department of Population Health, University of Oxford, Oxford, UK., United Kingdom
  • Ioanna Tzoulaki, Imperial College, MRC-PHE Centre for Environment and Health, Dept of Epidemiology and Biostatistics, School of Public Health, Imperial College, London, United Kingdom., University of Ioannina Medical School, Ioannina, United Kingdom
  • Elio Riboli, Imperial College, United Kingdom
  • David C Muller, Imperial College, United Kingdom

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.

Original languageEnglish
JournalCancer Epidemiology, Biomarkers & Prevention
Pages (from-to)507-512
Number of pages6
Publication statusPublished - Mar 2021

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ID: 207043703