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Endometrial Precancer

  • John A. Maksem
  • Stanley J. Robboy
  • John W. Bishop
  • Isabelle Meiers
Chapter
Part of the Essentials in Cytopathology book series (EICP, volume 7)

A situation generally dismissed as benign by pathologists and gynecologists alike is the clinically/sonographically thin endometrium that affords a paucicellular, essentially nondiagnostic (informatively noninformative) tissue specimen. Normal postmenopausal endometrium is grossly thin and microscopically inactive and oftentimes (in up to 50% of samplings) yields no tissue, but the corollary that grossly thin (sonographically <5-mm thickness) postmenopausal endometrium is microscopically inactive and uniformly benign may be false. Endometrial suction biopsy has a reasonable sensitivity and specificity for detecting symptomatic endometrial cancer in thickened endometrium. However, few data are available regarding its sensitivity and specificity among asymptomatic postmenopausal women.

While it is well understood that every endometrial cancer has a precursor state, the assumption that the precursor of endometrial carcinoma, similar to hyperplasia, must be diffuse and symptomatic is false. Theoretically, the cells of an endometrial precancer should be genetically different from normal endometrium yet should share some but not all the features of malignant endometrium. ­Precancerous endometrial cells should be neoplastic (Table 8.1) and demonstrate a monoclonal growth pattern and clonally distributed (geographically clustered) mutations. Progression of ­endometrial 
precancer to carcinoma, effectively a conversion from a benign neoplasm to a malignant neoplasm, would be expectedly accomplished through the acquisition of additional mutations and accompanied by a change in biological behavior most notably characterized by the ability of outspoken cancer cells to invade local tissues and to metastasize.

Keywords

Endometrial Cancer Endometrial Carcinoma Clear Cell Carcinoma Serous Carcinoma Endometrial Hyperplasia 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • John A. Maksem
    • 1
  • Stanley J. Robboy
    • 2
  • John W. Bishop
    • 3
  • Isabelle Meiers
    • 4
  1. 1.Orlando HealthOrlando
  2. 2.Duke University Medical CenterDurham
  3. 3.University of California, DavisSacramento
  4. 4.University Hospital LewishamLondonEngland

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