scholarly journals Molecular determinants for lymph node metastasis in clinically early-stage endometrial cancer

2015 ◽  
Vol 11 (1) ◽  
pp. 323-329 ◽  
Author(s):  
NADIM BOU ZGHEIB ◽  
DOUGLAS C. MARCHION ◽  
STEPHEN H. BUSH ◽  
PATRICIA L. JUDSON ◽  
ROBERT M. WENHAM ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Stage ◽  
Node Metastasis ◽  
Molecular Determinants ◽  
Early Stage Endometrial Cancer

Molecular determinants for lymph node metastasis in early-stage endometrial cancer

2013 ◽  
Vol 130 (1) ◽  
pp. e5
Author(s):  
N. Bou Zgheib ◽  
D. Marchion ◽  
I. Ramirez ◽  
P. Teefey ◽  
P. Judson Lancaster ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Stage ◽  
Node Metastasis ◽  
Molecular Determinants ◽  
Early Stage Endometrial Cancer

Direct Comparison of Logistic Regression and Recursive Partitioning to Predict Lymph Node Metastasis in Endometrial Cancer

2015 ◽  
Vol 25 (6) ◽  
pp. 1037-1043 ◽  
Author(s):  
Martin Koskas ◽  
Dominique Luton ◽  
Olivier Graesslin ◽  
Emmanuel Barranger ◽  
Françoise Clavel-Chapelon ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Endometrial Cancer ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Early Stage ◽  
Recursive Partitioning ◽  
Operating Characteristics ◽  
Node Metastasis ◽  
Validation Set ◽  
Early Stage Endometrial Cancer

ObjectiveThe purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict lymph node metastasis in early-stage endometrial cancer.Methods/MaterialsThree models (1 LRM and 2 RP, a simple and a complex) were built in a same training set extracted from the Surveillance, Epidemiology, and End Results database for 18,294 patients who underwent hysterectomy and lymphadenectomy for stage I or II endometrial cancer. The 3 models were validated in a same validation set of 499 patients. Model performance was quantified with respect to discrimination (evaluated by the areas under the receiver operating characteristics curves) and calibration.ResultsIn the training set, the areas under the receiver operating characteristics curves were similar for LRM (0.80 [95% confidence interval [CI], 0.79–0.81]) and the complex RP model (0.79 [95% CI, 0.78–0.80]) and higher when compared with the simple RP model (0.75 [95% CI, 0.74–0.76]). In the validation set, LRM (0.77 [95% CI, 0.75–0.79]) outperformed the simple RP model (0.72 [95% CI, 0.70–0.74]). The complex RP model had good discriminative performances (0.75 [95% CI, 0.73–0.77]). Logistic regression model also outperformed the simple RP model in terms of calibration.ConclusionsIn these real data sets, LRM outperformed the simple RP model to predict lymph node metastasis in early-stage endometrial cancer. It is therefore more suitable for clinical use considering the complexity of an RP complex model with similar performances.

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