BreastCancerCoimbra
Breast cancer is the most common malignancy among women worldwide. There is extensive literature on the relationship between body weight and breast cancer risk but some doubts still remain about the role of adipokines per se, the role of insulin and glucose regardless of obesity, as well as the crosstalk between these players. Thus, in this project, we intend to determine the relation between body mass index (BMI), glycaemia, insulinemia, insulin-resistance, blood adipokine levels and tumour.
Data Set Information:
There are 10 predictors, all quantitative, and a binary dependent variable, indicating the presence or absence of breast cancer. The predictors are anthropometric data and parameters which can be gathered in routine blood analysis. Prediction models based on these predictors, if accurate, can potentially be used as a biomarker of breast cancer.
Attribute Information:
Quantitative Attributes:
- Age (years)
- BMI (kg/m2)
- Glucose (mg/dL)
- Insulin (µU/mL)
- HOMA
- Leptin (ng/mL)
- Adiponectin (µg/mL)
- Resistin (ng/mL)
- MCP-1(pg/dL)
Labels:
- 1 = Healthy controls
- 2 = Patients
Abbreviations
- BC: Breast cancer
- BMI: Body mass index
- HOMA: Homeostasis Model Assessment
- MCP-1: Chemokine Monocyte Chemoattractant Protein 1
Goals
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. Nowadays in a medical test, the big indicators of success are specificity and sensitivity. Every medical test strives to reach 100% in both criteria.