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

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...

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Autores principales:	Marika Vezzoli, Antonella Ravaggi, Laura Zanotti, Rebecca Angelica Miscioscia, Eliana Bignotti, Monica Ragnoli, Angela Gambino, Giuseppina Ruggeri, Stefano Calza, Enrico Sartori, Franco Odicino
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6f39f5d3573549dba69d72b188518e9f
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spelling	oai:doaj.org-article:6f39f5d3573549dba69d72b188518e9f2021-12-02T15:04:51ZRERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients10.1038/s41598-017-11104-42045-2322https://doaj.org/article/6f39f5d3573549dba69d72b188518e9f2017-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-11104-4https://doaj.org/toc/2045-2322Abstract 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.Marika VezzoliAntonella RavaggiLaura ZanottiRebecca Angelica MisciosciaEliana BignottiMonica RagnoliAngela GambinoGiuseppina RuggeriStefano CalzaEnrico SartoriFranco OdicinoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marika Vezzoli Antonella Ravaggi Laura Zanotti Rebecca Angelica Miscioscia Eliana Bignotti Monica Ragnoli Angela Gambino Giuseppina Ruggeri Stefano Calza Enrico Sartori Franco Odicino RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
description	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.
format	article
author	Marika Vezzoli Antonella Ravaggi Laura Zanotti Rebecca Angelica Miscioscia Eliana Bignotti Monica Ragnoli Angela Gambino Giuseppina Ruggeri Stefano Calza Enrico Sartori Franco Odicino
author_facet	Marika Vezzoli Antonella Ravaggi Laura Zanotti Rebecca Angelica Miscioscia Eliana Bignotti Monica Ragnoli Angela Gambino Giuseppina Ruggeri Stefano Calza Enrico Sartori Franco Odicino
author_sort	Marika Vezzoli
title	RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
title_short	RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
title_full	RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
title_fullStr	RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
title_full_unstemmed	RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients
title_sort	rert: a novel regression tree approach to predict extrauterine disease in endometrial carcinoma patients
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6f39f5d3573549dba69d72b188518e9f
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RERT: A Novel Regression Tree Approach to Predict Extrauterine Disease in Endometrial Carcinoma Patients

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