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. 2023 Jan-Dec:30:10732748231202851.
doi: 10.1177/10732748231202851.

A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study

Shaochun Liu  1 , Yingxue Jia  1 , Jiaying Chai  1 , Han Ge  1 , Runze Huang  1 , Anlong Li  1 , Huaidong Cheng  1   2   3
Affiliations

A Predictive Model for the Early Death of Breast Cancer With Synchronous Liver Metastases: A Population-Based Study

Shaochun Liu et al. Cancer Control. 2023 Jan-Dec.

Abstract

Background: Breast cancer liver metastasis (BCLM) is a severe condition often resulting in early death. The identification of prognostic factors and the construction of accurate predictive models can guide clinical decision-making.

Methods: A large sample of data from the Surveillance, Epidemiology, and End Results (SEER) database was analyzed, including 3711 patients diagnosed with de novo BCLM between 2010 and 2015. Predictive models were developed using histograms, and stepwise regression addressed variable collinearity. Internal validation was performed, and results were compared to similar studies.

Results: In this study of 3711 BCLM patients, 2571 didn't have early death. Out of the 1164 who died early, 1086 had cancer-specific early death. Prognostic factors for early death, including age, race, tumor size, and lymph node involvement, were identified. A nomogram based on these factors was constructed, accurately predicting early all-cause and cancer-specific death.

Conclusions: Valuable insights into the prognosis of BCLM patients were provided, and important prognostic factors for early death were identified. The developed nomogram can assist clinicians in identifying high-risk patients for early death and inform treatment decisions.

Keywords: End Results; Surveillance, Epidemiology, and; breast cancer; liver metastases; nomogram; prognostic model.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Data selection flow chart for breast cancer liver metastasis patients in the SEER database.
Figure 2.
Figure 2.
Nomograms for predicting early death (all-cause in (A) and cancer-specific in (B)) in breast cancer liver metastasis patients.
Figure 3.
Figure 3.
Receiver operating characteristic (ROC) curves for discriminating nomograms in predicting early death (all-cause and cancer-specific) in the training cohort (A, B) and the validation cohort (C, D).
Figure 4.
Figure 4.
Calibration curves for evaluating the calibration of the nomograms in predicting early death (all-cause and cancer-specific) in the training cohort (A, B) and the validation cohort (C, D).
Figure 5.
Figure 5.
Decision curve analysis (DCA) for nomograms in predicting early death (all-cause and cancer-specific) in the training cohort (A, B) and the validation cohort (C, D).
See this image and copyright information in PMC

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