JOURNAL ARTICLE

Comprehensive lymphadenectomy and survival prediction in uterine serous cancer patients after surgery: A population-based analysis

Hui Li, Xiaofei Xie, Yunyun Liu, Xiaoxin Huang, Haoliang Lin, Jing Li, Zhongqiu Lin
European Journal of Surgical Oncology 2020, 46 (7): 1339-1346
32402510

OBJECTIVE: Evidence on uterine serous cancer (USC) prognosis has been limited and inconclusive. We aim to explore the survival benefits of comprehensive lymphadenectomy in USC patients after surgery and develop a prognostic nomogram to predict survival.

METHODS: USC patients who had undergone hysterectomy between 2010 and 2015 were identified from Surveillance, Epidemiology and End Results (SEER) database. The relationship between the extent of lymphadenectomy and survival, including overall survival (OS) and cancer-specific survival (CSS), was estimated with Kaplan-Meier (K-M) analysis. Univariate and multivariate Cox regression analyses were utilized to determine the independent prognostic factors. A nomogram was then developed, calibrated and internally validated.

RESULTS: A total of 2853 patients were identified. K-M survival analysis revealed that patients with ≥12 pelvic lymph nodes (PLNs) removed had significantly better OS and CSS than those without (both P < 0.001). However, patients with ≥6 para-aortic lymph nodes removed was not associated with similar survival benefits than patients without (P > 0.1). Multivariate analyses for OS and CSS revealed that age, T-stage, N-stage, tumor size, adjuvant therapy and ≥12 PLNs removed were independent prognostic factors (all P < 0.05) and were subsequently incorporated into the nomogram. The Harrell's C-index of the nomogram was significantly higher than that of the FIGO staging system (OS: 0.739 vs 0.671, P < 0.001; CSS: 0.752 vs 0.695, P < 0.001). Furthermore, the nomogram was well calibrated with satisfactory consistency.

CONCLUSIONS: Comprehensive pelvic lymphadenectomy should be recommended to USC patients for its survival benefits. And a nomogram has been developed to predict the survivals of USC patients after surgery.

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