Using Resistin, glucose, age and BMI to predict the presence of breast cancer
BMC cancer, 2018•Springer
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 …
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 …
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.
Springer
以上显示的是最相近的搜索结果。 查看全部搜索结果