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P60 Correlation of ganglionar affection predictive factors in endometrial cancer: a prospective cohort study
  1. I Aizpuru,
  2. M Mancisidor,
  3. M Gorostidi,
  4. R Ruiz,
  5. I Ruiz and
  6. A Lekuona
  1. Donostia Hospital, San Sebastian, Spain

Abstract

Introduction/Background Lymph node involvement is the most important prognostic factor in endometrial cancer. Identifying predictive factors of involvement prior to surgery would help us to plan the surgery and even avoid lymphadenectomy in low risk patients.

The aim of this study is to analyze the pre- and post-surgery correlation of these predictive factors and their association with lymph node involvement.

Methodology Prospective cohort study including 207 patients with endometrial cancer at Donostia University Hospital (June 2014 - December 2018). Inclusion criteria was indication for primary surgery.

Pre-surgery variables to study were histology (Endometroid subtype defined as type I and the rest as type II) and tumor grade (G1, G2 and G3) determined in preoperative endometrial biopsy, size and myometrial infiltration (MI) <or ≥ 50% by MRI. Then we analyzed the previous ones and lymphovascular space involvement (LVSI) in the definitive surgical piece.

Results The correlation was very high for the size (rho 0.98) (figure 1), high for the IM (rho 0.66) and the G (rho 0.66). The last one, became very high eliminating the G2 (rho 0.85). For histology, the correlation was moderate (rho 0.54).

All factors were associated with lymph node involvement (table 2). Best predictive model was constructed by a multivariate regression combining the LVSI (OR 2.4 CI 95% 1.0– 5.6), ≥50% myometrial infiltration (OR 6.0 CI 95% 2.5–14.3) and type II histology (OR 3.5 CI 95% 1.2–9.9).

Finally, we assessed the diagnostic output of tumor size for lymph node involvement using the ROC curve and its area under the curve was 0.68. (Figure 2).

Conclusion Studied predictive factors have good pre- and post-surgery correlation. All are associated with lymph node involvement. The best model to predict lymph node involvement is the one that includes MI, histology and LVSI.

Disclosure Nothing to disclose.

Abstract P60 Table 1

Clinical and pathological characteristics of patients

Abstract P60 Table 2

Predictors of lymph node involvement (univariate regression)

Abstract P60 Figure 1

Positive correlation between tumor size by MRI and in the definitive histology of the surg

Abstract P60 Figure 2

ROC curve. Association of tumor size with lymph node involvement

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