Early death prediction model for breast cancer with synchronous lung metastases: an analysis of the SEER database

Abstract

Background: Breast cancer with lung metastases (BCLM) is a serious condition that often leads to early death. This study aims to screen the risk factors of early death in BCLM patients and establish a simple and accurate nomogram prediction model. Identifying prognostic markers and developing accurate prediction models can help guide clinical decision-making.

Methods: The Surveillance, Epidemiology, and End Results (SEER) database was used to analyze a sizable sample of data, encompassing 4,238 BCLM patients diagnosed between 2010 and 2015. Stepwise regression was used to manage the collinearity of variables and to construct a prediction model based on the histogram. The results were subjected to internal validation and contrasted with those of related investigations.

Results: Of the 4,238 BCLM patients in this study, 3,232 did not die early. Of the 1,006 premature deaths, 891 were cancer specific. Lymph node involvement, tumor size, age, and race were all recognized as prognostic markers for premature mortality. A nomogram was constructed based on these factors to reliably predict cancer-specific death and early all-cause death.

Conclusions: This study gives new insights into the prognosis of individuals with BCLM and finds critical prognostic variables for early mortality. The created nomogram might assist physicians in identifying individuals at high risk of early mortality and making treatment options.

Keywords: Breast cancer; Surveillance, Epidemiology, and End Results (SEER); early death; lung metastases; nomogram.

Figure 1

Flowchart of patient eligibility and cohort division.

Figure 2

Nomograms to forecast early mortality in patients with lung metastases from breast cancer [all-cause in (A) and cancer-specific in (B)]. AJCC, American Joint Committee on Cancer; PR, progesterone receptor; HR, hormone receptor; HER2, human epidermal growth factor receptor 2.

Figure 3

ROC curves in the training cohort (A,B) and validation cohort (C,D) for differentiating nomograms in predicting early mortality [all-cause (A,C) and cancer-specific (B,D)]. Data on the blue line represent the cutoff value (specificity, sensitivity), and the expression of the AUC is AUC (95% confidence interval). ROC, receiver operating characteristic; AUC, area under the curve.

Figure 4

Calibration curves for assessing the calibration of the nomograms in forecasting early death (all-cause and cancer-specific) in the training cohort (A,B) and the validation cohort (C,D).

Figure 5

DCA was performed on nomograms to predict early death (all-cause and cancer-specific) in the training cohort (A,B) and validation cohort (C,D). DCA, decision curve analysis.