Background: Identification of aggressive endometrioid endometrial carcinomas (EECs) and
non-endometrioid carcinomas (NEECs) is essential to improve outcome. L1 cell adhesion …

Introduction Endometrial cancer (EC) is the most common gynecological cancer with a rising
incidence, attributed to advanced life expectancy and obesity. Adipose tissue (AT) is an …

Background Bayesian networks (BNs) are machine-learning–based computational models
that visualize causal relationships and provide insight into the processes underlying disease …

Objectives Endometrial carcinoma mortality is mainly caused by recurrent disease, and
various immunohistochemical markers to predict recurrences have been studied. Loss of the …

Endometrial carcinomas are histologically classified as endometrioid, assumed to originate
from hyperplastic endometrium, or non-endometrioid carcinomas, assumed to originate from …

Objective Pre-operative immunohistochemical (IHC) biomarkers are not incorporated in
endometrial cancer (EC) risk classification. We aim to investigate the added prognostic …

Importance Patients with low-grade (ie, grade 1-2) endometrial cancer (EC) are
characterized by their favorable prognosis compared with patients with high-grade (ie, grade …

Background Oestrogen receptor (ER) expression is a prognostic biomarker in endometrial
cancer (EC). However, expression does not provide information about the functional activity …

Endometrial carcinoma (EC) is traditionally diagnosed by a histopathological assessment of
an endometrial biopsy, leaving up to 30% of patients undiagnosed due to technical failure or …

Aims For the diagnostic workup of postmenopausal bleeding, histological examination of the
endometrium is frequently performed. Failure of endometrial sampling due to insufficient …