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. 2018 Jan 4;18(1):29.
doi: 10.1186/s12885-017-3877-1.

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

Miguel Patrício  1 , José Pereira  2 , Joana Crisóstomo  3 , Paulo Matafome  3   4 , Manuel Gomes  5 , Raquel Seiça  3 , Francisco Caramelo  6
Affiliations

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

Miguel Patrício et al. BMC Cancer. .

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.

Keywords: Age; BMI; Biomarker; Breast cancer; Glucose; Resistin.

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Conflict of interest statement

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the University Hospital Centre of Coimbra and performed in accordance with the Declaration of Helsinki. All patients gave their written informed consent prior to entering the study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Flowchart of the computer routine for assessing the performance of each classification method when applied to n features
Fig. 2
Fig. 2
Profiles of the clinical features of features of patients with breast cancer (n = 64) and healthy controls (n = 52). BMI - body mass index; MCP-1 - monocyte chemoattractant protein-1, HOMA - homeostasis model assessment for insulin resistance
Fig. 3
Fig. 3
ROC curves corresponding to the best and worst Logistic Regression (LR) models generated with four predictors in the cross-validation procedure
Fig. 4
Fig. 4
ROC curves corresponding to the best and worst Random Forest (RF) models generated with four predictors in the cross-validation procedure
Fig. 5
Fig. 5
ROC curves corresponding to the best and worst Support Vector Machine (SVM) models generated with four predictors in the cross-validation procedure
See this image and copyright information in PMC

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