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Risk prediction models for endometrial cancer: development and validation in an international consortium.
Pubmed ID
36688725 (View this publication on the PubMed website)
Digital Object Identifier
J Natl Cancer Inst. 2023 Jan 23
Shi J, Kraft P, Rosner B, Benavente Y, Black A, Brinton LA, Chen C, Clarke MA, Cook LS, Costas L, Dal Maso L, Freudenheim JL, Frias-Gomez J, Friedenreich CM, Garcia-Closas M, Goodman MT, Johnson L, La Vecchia C, Levi F, Lissowska J, more Lu L, McCann SE, Moysich KB, Negri E, O'Connell K, Parazzini F, Petruzella S, Polesel J, Ponte J, Rebbeck TR, Reynolds P, Ricceri F, Risch H, Sacerdote C, Setiawan VW, Shu XO, Spurdle AB, Trabert B, Webb PM, Wentzensen N, Wilkens LR, Xu WH, Yang HP, Yu H, Du M, De Vivo I
  • Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Cancer Epidemiology Research Programme, Catalan Institute of Oncology, Bellvitge Biomedical Research Institute, Barcelona, Spain.
  • Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA.
  • Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
  • Department of Epidemiology, Colorado School of Public Heath, University of Colorado-Anschutz, Aurora, Colorado, USA.
  • Cancer Epidemiology Unit, Centro di Riferimento Oncologico di Aviano, IRCCS, Aviano, Italy.
  • Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, The State University of New York at Buffalo, Buffalo, New York, USA.
  • Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, Alberta, Canada.
  • Cedars-Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, California, USA. more
  • Department of Clinical Medicine and Community Health, Università degli Studi di Milano, Milan, Italy.
  • Department of Epidemiology and Health Services Research, Centre for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.
  • Department of Cancer Epidemiology and Prevention, M. Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
  • Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, Connecticut, USA.
  • Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA.
  • Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York.
  • Division of Population Science, Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.
  • Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California, USA.
  • Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy.
  • Unit of Cancer Epidemiology, Città della Salute e della Scienza University-Hospital and Center for Cancer Prevention (CPO), Turin, Italy.
  • Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA.
  • Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
  • Population Health Department, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
  • Epidemiology Program, University of Hawaii Cancer Center, Honolulu, Hawaii, USA.
  • Department of Epidemiology, Fudan University School of Public Health, Shanghai, China.

BACKGROUND: Endometrial cancer risk stratification may help target interventions, screening, or prophylactic hysterectomy to mitigate the rising burden of this cancer. However, existing prediction models have been developed in select cohorts and have not considered genetic factors.

METHODS: We developed endometrial cancer risk prediction models using data on postmenopausal white women aged 45-85 years from 19 case-control studies in the Epidemiology of Endometrial Cancer Consortium. Relative risk estimates for predictors were combined with age-specific endometrial cancer incidence rates and estimates for the underlying risk factor distribution. We externally validated the models in three cohorts: Nurses' Health Study (NHS), Nurses' Health Study II (NHS II) and the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

RESULTS: Area under the receiver operating characteristic curves for the epidemiologic model ranged from 0.64 (95% CI: 0.62, 0.67) to 0.69 (95% CI: 0.66, 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in AUC in NHS,; PLCO: 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall E/O = 1.09; 95% CI: 0.98, 1.22) and PLCO (overall E/O = 1.04; 95% CI: 0.95, 1.13) but poorly calibrated in NHS (overall E/O = 0.55; 95% CI: 0.51, 0.59).

CONCLUSION: Using data from the largest, most heterogeneous study population to date, prediction models based on epidemiologic factors alone successfully identified women at high risk of endometrial cancer. Genetic factors offered limited improvements in discrimination. Further work is needed to refine this tool for clinical or public health practice and expand these models to multiethnic populations.

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