Validity of Brain Metastasis Risk Predictive Model in Hormone Positive Breast Cancer Patients

Overview

This study aims to validate a prediction model of brain metastasis risk for females with HR+ breast cancer by using variables collected at diagnosis.

1. Data collection: Age at BC diagnosis, tumor size, axillary adenopathy, clinical stage, HER2 status, Ki67 proliferation index and the modified Scarff-Bloom-Richardson grade of differentiation. 2. Analysis Plan: Continuous variables as means and standard deviation (SD) if they follow a normal distribution; if not, as medians and IQR (interquartile range). Numbers and percentages display categorical variables. 3. Model validation: The association between covariates and the diagnosis of brain metastases by performing logistic regression analyses; odds ratio (OR), 95 % confidence intervals (CI), and p-value will be estimated. Variables with a p-value \< 0.05 in the univariate logistic regression analysis will be further assessed in a multivariate logistic regression analysis. For the clinical use of the model, we will use a score to each variable as that designed by Cacho-Díaz B. et. al. then, we will categorize patients into three risk groups according to the sum of the scores. We will employe a Cox regression analysis to assess each group's risk of developing brain metastases. Adjusted hazard ratios (aHRs), 95 % CIs, and p values will be evaluated.

Conditions

Eligibility

Outcomes

Central Contacts