Predicting survival and prognosis of postoperative breast cancer brain metastasis: a population-based retrospective analysis

Abstract

Background: Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness.

Methods: From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.

Results: LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001).

Conclusion: The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

Figure 1

Selection of factors associated with OS using the LASSO Cox regression model. (A) The upper Abscissa is the number of non-zero coefficients in this model, and the ordinate is the coefficient value. LASSO coefficients of 25 candidate variables (age, laterality, different nationalities, tumor size, lymph node metastasis, neoadjuvant therapy, surgery method, grade, molecular subtype, ER, PR, HER2, Ki67, chemotherapy, target, endocrine, radiation, liver metastasis, lung metastasis, bone metastasis, vascular invasion, nerve invasion, and marital, menstrual, and vital status), including dummy variables in the training cohort. (B) The optimal penalization coefficient (λ) in the LASSO model was identified by 10-fold cross-validation and the minimum criterion in the training cohort. The left vertical dotted line represents the minimum error, and the right line represents the cross-validated error within one standard error of the minimum. The upper Abscissa indicates the number of independent variables that still exist in the model. ER: Estrogen receptor; HER2: Human epidermal growth factor receptor-2; LASSO: Least absolute shrinkage and selection operator; OS: Overall survival; PR: Progesterone receptor.

Figure 2

The nomogram of predicting OS in patients with postoperative BCBM. (A) Survival nomogram for the prediction of 1-year, 3-year, and 5-year OS in BM patients. (B) Dynamic nomogram of predicting OS in patients with postoperative BCBM. BM: Brain metastasis; BCBM: Breast cancer brain metastasis; HER2: Human epidermal growth factor receptor 2; OS: Overall survival; TNBC: Triple negative breast cancer.

Figure 3

The calibration curve of OS was predicted by the training, validation, and SEER groups of BCBM patients. 1-year, 3-year, and 5-year OS in the training cohort (A–C; C-index = 0.714), validation cohort (D–F; C-index = 0.710), and SEER cohort (G–I; C-index = 0.670). BCBM: Breast cancer brain metastasis; d: Number of deaths; n: Number of cases; OS: Overall survival; P: Sample size per calculation; SEER: Surveillance, Epidemiology, and End Results Program.

Figure 4

Predictive performance of the survival nomogram of BCBM patients reflected by ROC curves. ROC curves for the 1-, 3- and 5-year OS in patients in the training cohort (A–C), validation cohort (D–F), and SEER cohort (G–I). AUC: Area under the curve; BCBM: Breast cancer brain metastasis; OS: Overall survival; ROC: Receiver operating characteristic; SEER: Surveillance, Epidemiology, and End Results Program.

Figure 5

Kaplan–Meier curve to test the stratification system of BCBM in the training cohort (A), validation set (B), and SEER set (C). BCBM: Breast cancer brain metastasis; SEER: Surveillance, Epidemiology, and End Results Program.