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#389 Seneca study: staging endometrial cancer based on molecular classification
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  1. Enrique Chacon,
  2. Felix Boria,
  3. Luisa Sanchez-Lorenzo,
  4. Antonio Gonzalez Martín,
  5. Jose Angel Minguez and
  6. Luis Chiva
  1. Clinica Universidad de Navarra, Madrid, Spain

Abstract

Introduction/Background Endometrial Cancer (EC) management is evolving and understanding the rate of Sentinel Lymph Node (SLN) involvement based on molecular subgroups is critical for accurate staging. Our study aims to evaluate SLN involvement rates in early-stage (FIGO I/II) EC, considering the molecular subtypes. Additionally, we will assess SLN involvement for each prognostic risk group according to the new ESGO classification.

Methodology SENECA study is a retrospective multicentric international observational study reviewing data from 2139 women with presurgical stage I-II endometrial cancer across 64 centers in 17 countries. Between January 2021 and December 2022, patients underwent surgical treatment with SLN assessment, following ESGO guidelines. SLN study protocols were accredited using either ultrastaging or OSNA.

Results Among the 2139 patients, the molecular subgroups were as follows: 272 (12.7%) p53 abnormal, 1191 (55.7%) NSMP, 525 (24.5%) MMRd, 55 (2.6%) POLE ultramutated, and 96 (4.5%) Multiple Classifier cases. The bilateral SLN detection rate was 80.8%, SLN involvement was found in 205 patients (9.6%). Notably, the rate of SLN involvement varied significantly depending on the molecular group (p53 12.50%, NSMP 7.81%, MMRd 12.19%, POLE ultramutated 7.27%, Multiple Classifier 10.42%, p=0.021). Additionally, within the new ESGO risk groups, including molecular classification, the risk of SLN involvement differed substantially: low-risk group 2.8%, intermediate 6.6%, high intermediate 21.6%, and high-risk group 22.5% (p=0.001).

Conclusion Our study reveals important differences in SLN involvement among patients with early-stage EC based on their molecular subtypes. These findings emphasize the significance of considering molecular characteristics to ensure accurate staging and optimize management decisions for patients with endometrial cancer.

Disclosures No conflict of interest.

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