ArticleBMC women's health2026
Preoperative clinical predictors of lymph node metastasis in endometrial cancer: a retrospective cohort study.
Article in BMC women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundMany low-risk endometrial cancer (EC) patients will get no significant benefits from unneeded lymphadenectomy in addition to increasing morbidity and lymphedema risks, which worsen patients’ prognosis. In the present study, we aimed to develop a non-invasive model for the detection of lymph node metastases in endometrial cancer patients using relatively simple clinical predictors. PATIENTS AND
methodsWe included a total of 100 EC patients. We collect the demographic parameters, serologic markers, imaging parameters, and FIGO pathologic staging. Preoperative pathological assessment of the endometrial biopsy was evaluated. We considered grades 1 and 2 endometrioid EC as low-grade endometrial carcinoma (LGEC), and another histological grade as high-grade endometrial carcinoma (HGEC). We correlate between patients with and without lymph node metastases regarding all detected parameters.
resultsOf the 100 patients, 53 had lymph node metastases, and 47 had no lymph node metastases. Radiological evidence of enlarged lymph nodes, large tumor volume, lympho-vascular invasion (LVI), and high levels of serum CA125 (U/mL) were positively associated with the presence of lymph node metastases (p < 0.001). Patients with pathological evidence of lymph node metastases, who were predicted to have lymph node metastases preoperatively based on clinical and radiological findings, demonstrated less favorable prognostic outcomes compared to those with negative models of prediction.
conclusionsHigh serum CA125, enlarged lymph nodes, and large tumor volume were positively associated with the presence of lymph node metastases. While other factors, such as deep myometrial invasion, deep cervical stromal invasion, showed significant associations. This predictive model may assist clinicians in identifying high-risk patients who truly benefit from lymphadenectomy, while sparing low-risk patients from unnecessary procedures and their associated complications.
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