Objective: The purpose was to compare logistic regression model (LRM) and recursive partitioning (RP) to predict lymph node metastasis in early-stage endometrial cancer.
Methods/materials: Three 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.
Results: In 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.
Conclusions: In 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.