Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/107516
Title: Using Resistin, glucose, age and BMI to predict the presence of breast cancer
Authors: Patrício, Miguel 
Pereira, José
Crisóstomo, Joana 
Matafome, Paulo N. 
Gomes, Manuel
Seiça, Raquel
Caramelo, Francisco 
Keywords: Breast cancer; Glucose; Resistin; BMI; Age; Biomarker
Issue Date: 4-Jan-2018
Publisher: Springer Nature
Project: UID/NEU/04539/2013 
PTDC/SAU-MET/121133/2010 
Serial title, monograph or event: BMC Cancer
Volume: 18
Issue: 1
Abstract: Background: The goal of this exploratory study was to develop and assess a prediction model which can potentially be used as a biomarker of breast cancer, based on anthropometric data and parameters which can be gathered in routine blood analysis. Methods: For each of the 166 participants several clinical features were observed or measured, including age, BMI, Glucose, Insulin, HOMA, Leptin, Adiponectin, Resistin and MCP-1. Machine learning algorithms (logistic regression, random forests, support vector machines) were implemented taking in as predictors different numbers of variables. The resulting models were assessed with a Monte Carlo Cross-Validation approach to determine 95% confidence intervals for the sensitivity, specificity and AUC of the models. Results: Support vector machines models using Glucose, Resistin, Age and BMI as predictors allowed predicting the presence of breast cancer in women with sensitivity ranging between 82 and 88% and specificity ranging between 85 and 90%. The 95% confidence interval for the AUC was [0.87, 0.91]. Conclusions: These findings provide promising evidence that models combining age, BMI and metabolic parameters may be a powerful tool for a cheap and effective biomarker of breast cancer.
URI: https://hdl.handle.net/10316/107516
ISSN: 1471-2407
DOI: 10.1186/s12885-017-3877-1
Rights: openAccess
Appears in Collections:I&D IBILI - Artigos em Revistas Internacionais
FMUC Medicina - Artigos em Revistas Internacionais

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