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

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Uterine Cancer
Direct Comparison of Logistic Regression and Recursive Partitioning to Predict Lymph Node Metastasis in Endometrial Cancer
  1. Martin Koskas, MD, PhD*,†,‡,
  2. Dominique Luton, MD, PhD*,†,
  3. Olivier Graesslin, MD, PhD§,
  4. Emmanuel Barranger, MD, PhD∥,
  5. Françoise Clavel-Chapelon, PhD,
  6. Bassam Haddad, MD, PhD#,
  7. Emile Darai, MD, PhD** and
  8. Roman Rouzier, MD, PhD‡,††
  1. *Department of Obstetrics and Gynaecology, APHP Hôpital Bichat, Paris, France;
  2. †Paris Diderot University Paris 07, Paris, France;
  3. ‡EA 7285, UVSQ, Poissy, France;
  4. §Department of Obstetrics and Gynaecology, CHU Reims, Reims, France;
  5. ∥Department of Obstetrics and Gynaecology, APHP Hôpital Lariboisiere, Paris, France;
  6. INSERM UMR-S 1018, Université Paris-Sud, Institut Gustave-Roussy, Villejuif, France;
  7. #Department of Obstetrics and Gynaecology, CHIC, Créteil, France;
  8. **Department of Obstetrics and Gynaecology, APHP Hôpital Tenon, Paris, France; and
  9. ††Department of Gynaecology Institut Curie, Paris, France.
  1. Address correspondence and reprint requests to Martin Koskas, MD, PhD, Department of Obstetrics and Gynecology, CHU Bichat Claude Bernard, 46, rue Henri-Huchard, 75018 Paris, France. E-mail: martin.koskas{at}bch.aphp.fr; martin.koskas{at}wanadoo.fr.

Abstract

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.

  • Endometrial cancer
  • Lymph node metastasis
  • Score
  • Prediction
  • Recursive partitioning

https://doi.org/10.1097/IGC.0000000000000451

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    • Supplemental digital content is available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.ijgc.net).

    • The authors declare no conflicts of interest.