Prediction models for endometrial cancer for the general population or symptomatic women: A systematic review

Abstract

Objective: To provide an overview of prediction models for the risk of developing endometrial cancer in women of the general population or for the presence of endometrial cancer in symptomatic women. Methods: We systematically searched the Embase and Pubmed database until September 2017 for relevant publications. We included studies describing the development, the external validation, or the updating of a multivariable model for predicting endometrial cancer in the general population or symptomatic women. Results: Out of 2756 references screened, 14 studies were included. We found two prediction models for developing endometrial cancer in the general population (risk models) and one extension. Eight studies described the development of models for symptomatic women (diagnostic models), one comparison of the performance of two diagnostic models and two external validation. Sample size varied from 60 (10 with cancer) to 201,811 (855 with cancer) women. The age of the women was included as a predictor in almost all models. The risk models included epidemiological variables related to the reproductive history of women, hormone use, BMI, and smoking history. The diagnostic models also included clinical predictors, such as endometrial thickness and recurrent bleeding. The concordance statistic (c), assessing the discriminative ability, varied from 0.68 to 0.77 in the risk models and from 0.73 to 0.957 in the diagnostic models. Methodological information was often limited, especially on the handling of missing data, and the selection of predictors. One risk model and four diagnostic models were externally validated. Conclusions: Only a few models have been developed to predict endometrial cancer in asymptomatic or symptomatic women. The usefulness of most models is unclear considering methodological shortcomings and lack of external validation. Future research should focus on external validation and extension with new predictors or biomarkers, such as genetic and epigenetic markers.

New perspectives on screening and early detection of endometrial cancer

“…Adding serum biomarkers of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines did not relevantly improve discrimination of risk prediction models . The models in women with postmenopausal bleeding greatly varied and included a very different range of predictors . Given that 90% of endometrial cancers present with postmenopausal bleeding, early detection strategies focused on symptomatic women have the potential to capture a considerably large proportion of endometrial cancers.…”

“…Several prediction models for the risk of developing endometrial cancer have been evaluated in general population, and in women with postmenopausal bleeding . Prediction models can help identifying asymptomatic women at high risk of developing endometrial carcinoma, and as well facilitating early diagnosis in symptomatic women.…”

“…Prediction models can help identifying asymptomatic women at high risk of developing endometrial carcinoma, and as well facilitating early diagnosis in symptomatic women. However, current models usually lack external validation, and should probably be extended with new genetic and epigenetic biomarkers in order to improve models performance . Among general population, age and body mass index (BMI) were the main predictors of endometrial carcinoma risk, and reproductive history and exogenous hormone use were also relevant predictors .…”

“…Several prediction models for the risk of developing endometrial cancer have been evaluated in general population, [56][57][58] and in women with postmenopausal bleeding. [59][60][61][62] Prediction models can help identifying asymptomatic women at high risk of developing endometrial carcinoma, and as well facilitating early diagnosis in symptomatic women. However, current models usually lack external validation, and should probably be extended with new genetic and epigenetic biomarkers in order to improve models performance.…”

“…However, current models usually lack external validation, and should probably be extended with new genetic and epigenetic biomarkers in order to improve models performance. 59 Among general population, age and body mass index (BMI) were the main predictors of endometrial carcinoma risk, 56,57 and reproductive history and exogenous hormone use were also relevant predictors. 56 Adding serum biomarkers of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines did not relevantly improve discrimination of risk prediction models.…”