A preoperative risk-scoring system to predict lymph node metastasis in endometrial cancer and stratify patients for lymphadenectomy

Abstract Background The authors aimed to create a novel model to predict lymphatic metastasis in thymic epithelial tumors. Methods Data of 1018 patients were collected from the Surveillance, Epidemiology, and End Results database from 2004 to 2015. To construct a nomogram, the least absolute shrinkage and selection operator (LASSO) regression model was used to select candidate features of the training cohort from 2004 to 2013. A simple model called the Lymphatic Node Metastasis Risk Scoring System (LNMRS) was constructed to predict lymphatic metastasis. Using patients from 2014 to 2015 as the validation cohort, the predictive performance of the model was determined by receiver operating characteristic (ROC) curves. Results The LASSO regression model showed that age, extension, and histology type were significantly associated with lymph node metastasis, which were used to construct the nomogram. Through analysis of the area under the curve (AUC), the nomogram achieved a AUC value of 0.80 (95 % confidence interval [Cl] 0.75–0.85) in the training cohort and 0.82 (95 % Cl 0.70–0.93) in the validation cohort, and had closed calibration curves. Based on the nomogram, the authors constructed the LNMRS model, which had an AUC of 0.80 (95 % Cl 0.75–0.85) in the training cohort and 0.82 (95% Cl 0.70–0.93) in the validation cohort. The ROC curves indicated that the LNMRS had excellent predictive performance for lymph node metastasis. Conclusion This study established a nomogram for predicting lymph node metastasis. The LNMRS model, constructed to predict lymphatic involvement of patients, was more convenient than the nomogram.

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Predictive Value of Kanagawa Cancer Center Scoring System for Lymph Node Metastasis and Need for Lymphadenectomy in Patients With Endometrial Cancer: A Validation Study

International Journal of Gynecological Cancer ◽

10.1097/igc.0000000000001301 ◽

2018 ◽

Vol 28 (7) ◽

pp. 1290-1296 ◽

Cited By ~ 2

Author(s):

Mozhdeh Momtahan ◽

Marjan Hosseini ◽

Minoo Robati ◽

Fatemesadat Najib

Keyword(s):

Endometrial Cancer ◽

Lymph Node ◽

Lymph Node Metastasis ◽

Predictive Value ◽

Scoring System ◽

Tumor Volume ◽

Lymph Node Involvement ◽

Cancer Center ◽

Node Metastasis ◽

Node Involvement

ObjectivesThe objective of this study was to determine the predictive value of Kanagawa Cancer Center (KCC) scoring system for lymph node metastasis and need for lymphadenectomy in patients with endometrial cancer.MethodsThis cross-sectional study was conducted during a 2-year period in a gynecologic oncology referral center in Southern Iran. We included a total number of 94 patients with endometrial cancer. Preoperative assessment included tumor volume, myometrium invasion, histology, and CA125. The KCC was calculated for all the patients. All the patients underwent total abdominal hysterectomy and bilateral salpingo-oophorectomy along with dissection of pelvic and para-aortic lymph nodes. The histopathology of the dissected lymph nodes was considered as criterion standard, and the predictive value of KCC was evaluated accordingly.ResultsThe mean ± SD age of the patients was 56.8 ± 10.2 years. Overall, 26 patients (27.7%) tested positive for lymph node involvement. The sensitivity, specificity, positive predictive value, and negative predictive value of KCC for lymph node involvement was found to be 35.3%, 100%, 100%, and 64.7%, respectively. Overall, the predictive value according to the area under the curve measured by receiver operating characteristic curve was found to be 0.890 (0.823–0.956) indicative of moderate accuracy. Lymph node involvement was associated with higher Federation of Gynecology and Obstetrics stage (P< 0.001), higher tumor volume (P= 0.003), higher histological subtype (P< 0.001), positive CA125 (P< 0.001), and higher KCC score (P< 0.001).ConclusionsThe KCC scoring system has a moderate accuracy for predicting the lymph node involvement in patients with endometrial cancer.

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