Dataset Information

Risk prediction models for endometrial cancer: development and validation in an international consortium.

ABSTRACT:

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 (E2C2). 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 3 cohorts: Nurses' Health Study (NHS), 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% confidence interval [CI] = 0.62 to 0.67) to 0.69 (95% CI = 0.66 to 0.72). Improvements in discrimination from the addition of genetic factors were modest (no change in area under the receiver operating characteristic curves in NHS; PLCO = 0.64 to 0.66). The epidemiologic model was well calibrated in NHS II (overall expected-to-observed ratio [E/O] = 1.09, 95% CI = 0.98 to 1.22) and PLCO (overall E/O = 1.04, 95% CI = 0.95 to 1.13) but poorly calibrated in NHS (overall E/O = 0.55, 95% CI = 0.51 to 0.59).

Conclusions

Using data from the largest, most heterogeneous study population to date (to our knowledge), 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.

SUBMITTER: Shi J

PROVIDER: S-EPMC10165481 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Publications

Risk prediction models for endometrial cancer: development and validation in an international consortium.

Shi Joy J Kraft Peter P Rosner Bernard A BA Benavente Yolanda Y Black Amanda A Brinton Louise A LA Chen Chu C Clarke Megan A MA Cook Linda S LS Costas Laura L Dal Maso Luigino L Freudenheim Jo L JL Frias-Gomez Jon J Friedenreich Christine M CM Garcia-Closas Montserrat M Goodman Marc T MT Johnson Lisa L La Vecchia Carlo C Levi Fabio F Lissowska Jolanta J Lu Lingeng L McCann Susan E SE Moysich Kirsten B KB Negri Eva E O'Connell Kelli K Parazzini Fabio F Petruzella Stacey S Polesel Jerry J Ponte Jeanette J Rebbeck Timothy R TR Reynolds Peggy P Ricceri Fulvio F Risch Harvey A HA Sacerdote Carlotta C Setiawan Veronica W VW Shu Xiao-Ou XO Spurdle Amanda B AB Trabert Britton B Webb Penelope M PM Wentzensen Nicolas N Wilkens Lynne R LR Xu Wang Hong WH Yang Hannah P HP Yu Herbert H Du Mengmeng M De Vivo Immaculata I

Journal of the National Cancer Institute 20230501 5

<h4>Background</h4>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.<h4>Methods</h4>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 ...[more]

PMID: 36688725

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Project description:Because existing risk prediction models for lung cancer were developed in white populations, they may not be appropriate for predicting risk among African-Americans. Therefore, a need exists to construct and validate a risk prediction model for lung cancer that is specific to African-Americans. We analyzed data from 491 African-Americans with lung cancer and 497 matched African-American controls to identify specific risks and incorporate them into a multivariable risk model for lung cancer and estimate the 5-year absolute risk of lung cancer. We performed internal and external validations of the risk model using data on additional cases and controls from the same ongoing multiracial/ethnic lung cancer case-control study from which the model-building data were obtained as well as data from two different lung cancer studies in metropolitan Detroit, respectively. We also compared our African-American model with our previously developed risk prediction model for whites. The final risk model included smoking-related variables [smoking status, pack-years smoked, age at smoking cessation (former smokers), and number of years since smoking cessation (former smokers)], self-reported physician diagnoses of chronic obstructive pulmonary disease or hay fever, and exposures to asbestos or wood dusts. Our risk prediction model for African-Americans exhibited good discrimination [75% (95% confidence interval, 0.67-0.82)] for our internal data and moderate discrimination [63% (95% confidence interval, 0.57-0.69)] for the external data group, which is an improvement over the Spitz model for white subjects. Existing lung cancer prediction models may not be appropriate for predicting risk for African-Americans because (a) they were developed using white populations, (b) level of risk is different for risk factors that African-American share with whites, and (c) unique group-specific risk factors exist for African-Americans. This study developed and validated a risk prediction model for lung cancer that is specific to African-Americans and thus more precise in predicting their risks. These findings highlight the importance of conducting further ethnic-specific analyses of disease risk.

Dataset Information

Risk prediction models for endometrial cancer: development and validation in an international consortium.

Background

Methods

Results

Conclusions

Publications

Risk prediction models for endometrial cancer: development and validation in an international consortium.

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