2.2. Study Design
BRAIN METASTASIS, LUNG METASTASIS, BONE METASTASIS, REGRESSION, LIVER METASTASIS
The initial variables included age, race, marital status, laterality, histologic type, T stage, N stage, ER, PR, HER2, bone metastasis, liver metastasis, lung metastasis, brain metastasis, radiotherapy, and chemotherapy. Preliminary variable selection was conducted on the training group using three different approaches: univariate Cox hazard regression (implemented with the R package: survival), LASSO (utilizing the R package: glmnet), and BSR (employing the R package: leaps). Afterwards, a backward stepwise multivariable Cox regression analysis was performed to determine the final significant independent prognostic factors. In the univariate Cox hazard regression analysis, variables with a External validation of the nomogram was performed, and ROC curves and AUC values were used to evaluate the discrimination of the nomogram. Further analysis involved the calibration curves (resampling B = 1000) to assess the disparities between the predicted BCSS and actual BCSS in both the training and validation groups. Decision curve analysis was applied to measure the clinical benefits of the nomogram. Using a nomogram approach with the R package, nomogramFormula, we computed the BCSS risk scores for all patients, and then we stratified them into low-, intermediate-, and high-risk groups according to the optimal risk stratification cutoff values determined by the X-tile software (version 3.6.1; Yale, New Haven, CT, USA).