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.

Inclusion Criteria:

• Confirmed diagnosis of HR-positive primary invasive breast cancer.

Exclusion Criteria:

1. Patients if they were diagnosed with brain metastases at the time of initial breast cancer diagnosis or within the subsequent three months.
2. Bilateral breast cancer.
3. Synchronic cancer (i.e., lung cancer, melanoma).
4. Paitents presenting neurologic symptoms without follow-up or lacking a contrast-enhanced magnetic resonance image (cMRI).
5. Incomplete clinicopathological information at the time of diagnosis.

Medical records of women with HR+ breast cancer in clinical oncology department in Assiut University Hospital, Assiut, Egypt.

Not yet decided.