Dataset Information

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 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.

SUBMITTER: Vezzoli M

PROVIDER: S-EPMC5585365 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Vezzoli Marika M Ravaggi Antonella A Zanotti Laura L Miscioscia Rebecca Angelica RA Bignotti Eliana E Ragnoli Monica M Gambino Angela A Ruggeri Giuseppina G Calza Stefano S Sartori Enrico E Odicino Franco F

Scientific reports 20170905 1

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, histologica ...[more]

PMID: 28874808

Similar Datasets

Project description:Background & aimsCeliac disease is detected using serology and endoscopy analyses. We used multiple statistical analyses of a geographically isolated population in the United States to determine whether a single serum screening can identify individuals with celiac disease.MethodsWe performed a retrospective study of 3555 pediatric patients (18 years old or younger) in the intermountain West region of the United States from January 1, 2008, through September 30, 2013. All patients had undergone serologic analyses for celiac disease, including measurement of antibodies to tissue transglutaminase (TTG) and/or deamidated gliadin peptide (DGP), and had duodenal biopsies collected within the following year. Modified Marsh criteria were used to identify patients with celiac disease. We developed models to identify patients with celiac disease using logistic regression and classification and regression tree (CART) analysis.ResultsSingle use of a test for serum level of IgA against TTG identified patients with celiac disease with 90% sensitivity, 90% specificity, a 61% positive predictive value (PPV), a 90% negative predictive value, and an area under the receiver operating characteristic curve value of 0.91; these values were higher than those obtained from assays for IgA against DGP or IgG against TTG plus DGP. Not including the test for DGP antibody caused only 0.18% of celiac disease cases to be missed. Level of TTG IgA 7-fold the upper limit of normal (ULN) identified patients with celiac disease with a 96% PPV and 100% specificity. Using CART analysis, we found a level of TTG IgA 3.2-fold the ULN and higher to most accurately identify patients with celiac disease (PPV, 89%). Multivariable CART analysis showed that a level of TTG IgA 2.5-fold the ULN and higher was sufficient to identify celiac disease in patients with type 1 diabetes (PPV, 88%). Serum level of IgA against TTG in patients with versus those without trisomy 21 did not affect diagnosis predictability in CART analysis.ConclusionsIn a population-based study, we found that serum level of IgA against TTG can identify patients with celiac disease with PPVs of about 90%. Predictive values increase greatly when levels are markedly above the ULN or when the assay is used in combination with other variables. Measurement of IgG against TTG or DGP does not increase the accuracy of detection of celiac disease based against TTG IgA levels. There is a low risk of false-positive results from serologic analysis in patients with type I diabetes or persistent increases in antibody against TTG on repeat testing.

Project description:The identification and characterization of binding sites of DNA-binding molecules, including transcription factors (TFs), is a critical problem at the interface of chemistry, biology and molecular medicine. The Cognate Site Identification (CSI) array is a high-throughput microarray platform for measuring comprehensive recognition profiles of DNA-binding molecules. This technique produces datasets that are useful not only for identifying binding sites of previously uncharacterized TFs but also for elucidating dependencies, both local and nonlocal, between the nucleotides at different positions of the recognition sites. We have developed a regression tree technique, CSI-Tree, for exploring the spectrum of binding sites of DNA-binding molecules. Our approach constructs regression trees utilizing the CSI data of unaligned sequences. The resulting model partitions the binding spectrum into homogeneous regions of position specific nucleotide effects. Each homogeneous partition is then summarized by a position weight matrix (PWM). Hence, the final outcome is a binding intensity rank-ordered collection of PWMs each of which spans a different region in the binding spectrum. Nodes of the regression tree depict the critical position/nucleotide combinations. We analyze the CSI data of the eukaryotic TF Nkx-2.5 and two engineered small molecule DNA ligands and obtain unique insights into their binding properties. The CSI tree for Nkx-2.5 reveals an interaction between two positions of the binding profile and elucidates how different nucleotide combinations at these two positions lead to different binding affinities. The CSI trees for the engineered DNA ligands exhibit a common preference for the dinucleotide AA in the first two positions, which is consistent with preference for a narrow and relatively flat minor groove. We carry out a reanalysis of these data with a mixture of PWMs approach. This approach is an advancement over the simple PWM model and accommodates position dependencies based on only sequence data. Our analysis indicates that the dependencies revealed by the CSI-Tree are challenging to discover without the actual binding intensities. Moreover, such a mixture model is highly sensitive to the number and length of the sequences analyzed. In contrast, CSI-Tree provides interpretable and concise summaries of the complete recognition profiles of DNA-binding molecules by utilizing binding affinities.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets