Pathologic Prognostic Factors in Endometrial Carcinoma (Other Than Tumor Type and Grade)
McCluggage, W. Glenn
Quddus, M. Ruhul
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Although endometrial carcinoma (EC) is generally considered to have a good prognosis, over 20% of women with EC die of their disease, with a projected increase in both incidence and mortality over the next few decades. The aim of accurate prognostication is to ensure that patients receive optimal treatment
and are neither overtreated nor undertreated, thereby improving patient outcomes overall. Patients with EC can be categorized into prognostic risk groups based on clinicopathologic ﬁndings. Other than tumor type and grade, groupings and recommended management algorithms may take into account age, body mass index, stage, and presence of lymphovascular space invasion. The molecular classiﬁcation of EC that has emerged from the Cancer Genome Atlas (TCGA) study provides additional, potentially superior, prognostic information to traditional histologic typing and grading. This classiﬁer does not, however, replace clinicopathologic risk assessment based on parameters other than histotype and grade. It is envisaged that molecular and clinicopathologic prognostic grouping systems will work better together than either alone. Thus, while tumor typing and grading may be superseded by a classiﬁcation based on underlying genomic abnormalities, accurate assessment of other pathologic parameters will continue to be key to patient management. These include those factors related to staging, such as depth of myometrial invasion, cervical, vaginal, serosal surface, adnexal and parametrial invasion, and those independent of stage such as lymphovascular space invasion. Other prognostic parameters will also be discussed. These recommendations were developed from the International Society of Gynecological Pathologists Endometrial Carcinoma project.
Is part ofInternational Journal of Gynecological Pathology, 2019, vol. 38, núm. 1, p. S93–S113
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Except where otherwise noted, this item's license is described as cc-by (c) Naveena Singh et al., 2019
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