In the present study, the NCC-BCBM prognostic model was constructed and validated by using a large cohort of BCBM cases across the United States. This NCC-BCBM nomogram, based on currently available demographic, staging, and clinical therapeutic information can predict the survival probability for individual BCBM, which might be helpful for assisting clinicians in making therapeutic strategies.
Prognostic Predictors for BCLM
Previous population-based studies summarized prognostic factors and survival outcomes of breast cancer liver metastasis (BCLM). [21-24] Results showed histological grade 3 disease at primary presentation, advanced age and ER negative tumors predicted poor prognosis. However, cohort of these studies were small. A population-based study with large cohort analyzed factors for predicting the prognosis for BCLM. This study included 2098 cases and explored predictive factors with Kaplan-Meier analysis and multivariate analysis. Results showed age at diagnosis, marital status, surgery, tumor subtype, bone metastasis and brain metastasis were independent prognosis factors from a competing risk mode. Factors in this study including age, surgery, extra metastatic sites and tumor subtypes are consistent with the independent risk factors in our cohort. We also found race, radiation therapy, chemotherapy, laterality, grade AJCC T stage, and AJCC N stage were independent risk factors for OS. Generally, we found more independent risk factors for OS in our BCBM study than those of BCLM, and the reason might be the large cohort.
Nomogram prognostic model for BCBM
Several previous studies have created nomograms for patients with BCBM. Delpech et. al  retrospectively collected 314 BCBM patients and constructed the first clinical nomogram using Cox proportional hazards regression model to predict BCBM. Although the nomogram was validated and the concordance index was reported 0.69 in training group and 0.73 in external validation group, the cohort was too small to perform an accurate prediction. Another study analyzed BCBM patients from SEER database, created a predictive nomogram with 3311 cases from training cohort and validated the nomogram with 2549 cases from validation cohort.  Bootstraps with 1,000 resamples were also used to validate the nomograms in both cohorts. This study had the largest cohort with satisfied C-index of 0.705 and 0.678 in training and validation cohort, respectively, and could assist clinicians in predicting survival. Huang et.al analyzed risk factors of BC patients who developed bone metastasis with newly diagnosed infiltrating duct carcinoma (IDC), created a prognostic nomogram and validated it by ROC analysis. The AUCs showed 0.775, 0.758, and 0.731 in the training cohort and 0.770, 0.773, and 0.753 in the internal validation cohort; and 0.756, 0.764, and 0.767 in the external validation cohort for 1-, 3-, and 5-year OS, respectively. Generally, studies above used small cohorts for both building and validating the nomograms. In our study, we used 8635 cases as the training cohort and 4634 cases as the independent cohort. To the best of our knowledge, our study has the largest cohort of creating the nomogram and the largest cohort of validating the nomogram. C-index is a natural extension of ROC curve area. Our C-index for OS prediction reached 0.748, notably higher than previous studies.
Unavoidably, several flaws and limitations should be acknowledged in present study. First, this study only included patients with complete information, thus, selection bias might exist. Second, the SEER database does not have specific records about systemic therapy like endocrine and targeted therapy. Third, present study is retrospective, and internal and external validations were performed within SEER database. Therefore, prospective and external validations in cohorts outside SEER database are further needed.