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Sci Rep. 2017 Sep 5;7(1):10528. doi: 10.1038/s41598-017-11104-4.

RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients.

Author information

1
Department of Molecular and Translational Medicine, Unit of Biostatistics, University of Brescia, Brescia, Italy. marika.vezzoli@unibs.it.
2
"Angelo Nocivelli" Institute of Molecular Medicine, Division of Obstetrics and Gynecology, University of Brescia, Brescia, Italy. antonella.ravaggi@unibs.it.
3
"Angelo Nocivelli" Institute of Molecular Medicine, Division of Obstetrics and Gynecology, University of Brescia, Brescia, Italy.
4
Department of Obstetrics and Gynecology, University of Brescia, Brescia, Italy.
5
Division of Obstetrics and Gynecology, ASST Spedali Civili of Brescia, Brescia, Italy.
6
Laboratory Analysis, ASST Spedali Civili of Brescia, Brescia, Italy.
7
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.

Abstract

Some aspects of endometrial cancer (EC) preoperative work-up are still controversial, and debatable are the roles played by lymphadenectomy and radical surgery. Proper preoperative EC staging can help design a tailored surgical treatment, and this study aims to propose a new algorithm able to predict extrauterine disease diffusion. 293 EC patients were consecutively enrolled, and age, BMI, children's number, menopausal status, contraception, hormone replacement therapy, hypertension, histological grading, clinical stage, and serum HE4 and CA125 values were preoperatively evaluated. In order to identify before surgery the most important variables able to classify EC patients based on FIGO stage, we adopted a new statistical approach consisting of two-steps: 1) Random Forest with its relative variable importance; 2) a novel algorithm able to select the most representative Regression Tree (RERT) from an ensemble method. RERT, built on the above mentioned variables, provided a sensitivity, specificity, NPV and PPV of 90%, 76%, 94% and 65% respectively, in predicting FIGO stage > I. Notably, RERT outperformed the prediction ability of HE4, CA125, Logistic Regression and single cross-validated Regression Tree. Such algorithm has great potential, since it better identifies the true early-stage patients, thus providing concrete support in the decisional process about therapeutic options to be performed.

PMID:
28874808
PMCID:
PMC5585365
DOI:
10.1038/s41598-017-11104-4
[Indexed for MEDLINE]
Free PMC Article

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