Using Resistin, glucose, age and BMI to predict the presence of breast cancer

• Autor(a) principal: Patrício, Miguel
• Data de Publicação: 2018
• Outros Autores: Pereira, José, Crisóstomo, Joana, Matafome, Paulo N., Gomes, Manuel, Seiça, Raquel, Caramelo, Francisco
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10316/107516; https://doi.org/10.1186/s12885-017-3877-1
• topic: Breast cancer; Glucose; Resistin; BMI; Age; Biomarker; Aged; Blood Glucose; Body Mass Index; Breast Neoplasms; Female; Genetic Testing; Humans; Insulin; Insulin Resistance; Middle Aged; Obesity; Resistin

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.