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.

Mexican Multicenter Experience of Metastatic Spinal Disease

“…This coincides largely with the data obtained with the type of lesion identified in the most frequent primary tumors, except for the indeterminate type. The reason why the type of injury could not be identified could likely be operator-dependent viewing of the radiographs [36][37][38].…”

Prognostic factors of breast cancer brain metastasis

“…Nie et al [ 15 ] made prognosis prediction with LASSO Cox regression analysis. According to this analysis, the presence of lymph node metastasis, molecular subtype, tumor size, history of chemotherapy and radiotherapy, and the presence of lung metastasis were found to be important variables related to the development of brain metastasis.…”

Assessing of case–cohort design: a case study for breast cancer patients in Xinjiang, China

“…( 14 ) evaluated the association of hypoxia-inducible factor-1α and survivin with breast cancer prognosis in breast cancer patients. However, the sample size of some studies was relatively small ( 11 , 12 ), and those studies were mainly focus on exploring the impact of molecular subtypes or gene expression on the prognosis of breast cancer patients ( 11 , 13 , 14 ). On the other hand, it is necessary to follow up a large number of research subjects over the long-term in survival analysis, which may inevitably cause certain omissions in the process of data collection.…”

“…Relevant studies (8)(9)(10) showed that the current situation of breast cancer in Xinjiang is different from that in other regions, with such features as lower incidence rate, luminal breast cancer appearing more frequently, and women aged 45-55 having a higher risk of developing this disease. At present, there have been many studies evaluating the prognostic risk factors of patients with breast cancer in Xinjiang (11)(12)(13)(14); for instance, Shan et al (11) investigated the clinicopathological features and prognostic characteristics of patients with triple-negative breast cancer in Xinjiang, based on clinical information for 319 patients. Fu et al (13) focused on the difference in survival and prognosis of breast cancer patients with different molecular subtypes in Xinjiang.…”