Endometrial cancer: can nodal status be predicted with curettage?

Gynecol Oncol. 2005 Mar;96(3):594-600. doi: 10.1016/j.ygyno.2004.11.030.


Objective: To determine whether histologic or molecular markers assessed in pretreatment curettage specimens predict nodal metastasis in endometrial cancer.

Methods: Phenotypic and molecular variables (ploidy, proliferating cell nuclear antigen, MIB-1, p53, HER-2/neu, and bcl-2) were analyzed in preoperative specimens from 82 patients with endometrial cancer who had lymph nodes dissected. These 82 patients had been selected from a total population of 283 patients with endometrial cancer, using a case-cohort design. Weighted logistic regressions were then used to determine significant predictors of positive lymph nodes, and results were estimated for the total population of 283 patients.

Results: Of the overall population, 12% of patients were estimated to have positive lymph nodes. Histologic subtype, p53, and bcl-2 each were significantly correlated (P <0.05) with lymph node status. With application of stepwise logistic regression, p53 was the only independent predictor of lymph node status. In addition, a statistical model predictive of positive lymph nodes was generated which incorporated the risk factors p53, bcl-2, and histologic subtype.

Conclusion: In pretreatment curettage specimens, the presence of unfavorable levels of p53 or bcl-2 or of nonendometrioid histologic features, or combinations of those, significantly predicted lymph node status, thus facilitating the preoperative identification of patients at risk of lymph node metastases.

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Cell Cycle / physiology
  • Cohort Studies
  • Curettage / methods*
  • Endometrial Neoplasms / genetics
  • Endometrial Neoplasms / metabolism
  • Endometrial Neoplasms / pathology*
  • Endometrial Neoplasms / surgery*
  • Female
  • Humans
  • Immunohistochemistry
  • Lymph Nodes / pathology*
  • Lymphatic Metastasis
  • Middle Aged
  • Neoplasm Staging
  • Ploidies
  • Predictive Value of Tests