A novel model to estimate lymph node metastasis in endometrial cancer patients

OBJECTIVES: To evaluate the postoperative pathological characteristics of hysterectomy specimens, preoperative cancer antigen (CA)-125 levels and imaging modalities in patients with endometrial cancer and to build a risk matrix model to identify and recruit patients for retroperitoneal lymphadenectomy. METHODS: A total of 405 patients undergoing surgical treatment for endometrial cancer were retrospectively reviewed and analyzed. Clinical (age and body mass index), laboratory (CA-125), radiological (lymph node evaluation), and pathological (tumour size, grade, lymphovascular space invasion, lymph node metastasis, and myometrial invasion) parameters were used to test the ability to predict lymph node metastasis. Four parameters were selected by logistic regression to create a risk matrix for nodal metastasis. RESULTS: Of the 405 patients, 236 (58.3%) underwent complete pelvic and para-aortic lymphadenectomy, 96 (23.7%) underwent nodal sampling, and 73 (18%) had no surgical lymph node assessment. The parameters predicting nodal involvement obtained through logistic regression were myometrial infiltration >50%, lymphovascular space involvement, pelvic lymph node involvement by imaging, and a CA-125 value >21.5 U/mL. According to our risk matrix, the absence of these four parameters implied a risk of lymph node metastasis of 2.7%, whereas in the presence of all four parameters the risk was 82.3%. CONCLUSION: Patients without deep myometrial invasion and lymphovascular space involvement on the final pathological examination and with normal CA-125 values and lymph node radiological examinations have a relatively low risk of lymph node involvement. This risk assessment matrix may be able to refer patients with high-risk parameters necessitating lymphadenectomy and to decide the risks and benefits of lymphadenectomy.

Bibliographic Details

Main Authors:	Anton,Cristina, e Silva,Alexandre Silva, Baracat,Edmund Chada, Dogan,Nasuh Utku, Köhler,Christhardt, Carvalho,Jesus Paula, di Favero,Giovanni Mastrantonio
Format:	Digital revista
Language:	English
Published:	Faculdade de Medicina / USP 2017
Online Access:	http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1807-59322017000100030
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