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.
journal_name
BMC Cancerjournal_title
BMC cancerauthors
Patrício M,Pereira J,Crisóstomo J,Matafome P,Gomes M,Seiça R,Caramelo Fdoi
10.1186/s12885-017-3877-1subject
Has Abstractpub_date
2018-01-04 00:00:00pages
29issue
1issn
1471-2407pii
10.1186/s12885-017-3877-1journal_volume
18pub_type
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