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Early Death Incidence and Prediction in Stage IV Breast Cancer

Yumei Zhao, Guijun Xu, Xinpeng Guo, Wenjuan Ma, Yao Xu, Karl Peltzer, Vladimir P. Chekhonin, Vladimir P. Baklaushev, Nan Hu, Xin Wang, Zheng Liu, Chao Zhang

Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China (mainland)

Med Sci Monit 2020; 26:e924858

DOI: 10.12659/MSM.924858

Available online: 2020-06-15

Published: 2020-08-11


BACKGROUND: The early death of patients is a global cancer issue. We aimed to identify the risk factors for early death in stage IV breast cancer. Predictive nomograms for early death evaluation were generated based on the risk factors.
MATERIAL AND METHODS: Based on the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with IV breast cancer were selected. The risk factors for early death (survival time ≤1 year) were identified using logistic regression model analysis. Predictive nomograms were constructed and internal validation was performed.
RESULTS: A total of 5998 (32.6%) breast cancer patients were diagnosed as early death in the construction cohort. Age older than 50 years, unmarried status, black race, uninsured status, triple-negative type, grade (II and III), tumor size >5 cm, and metastasis to lung, liver, and brain were risk factors for total early death, while Luminal B subtype, N1 stage, and surgical interventions were associated with lower risk of early death. As for cancer-specific and non-cancer-specific early death, several factors were not consistent between the 2 groups. Nomograms for all-cause, cancer-specific, and non-cancer-specific early death were constructed. The calibration curve showed satisfactory agreement. The areas under the ROC curve (AUC) were 78.3% (95% CI: 77.7-78.9%), 75.8% (75.1-76.4%), and 72.3% (71.6-72.9%), respectively. In the validation cohort, a total of 689 (19.3%) patients were diagnosed as early death and the calibration curve showed satisfactory agreement. The AUCs of the all-cause, cancer-specific, and non-cancer-specific early death prediction were 74.0% (95% CI: 72.5-75.4%), 73.5% (72.0-74.9%), and 68.6% (67.0-70.1%), respectively.
CONCLUSIONS: Nomograms were generated to predict early death, with good calibration and discrimination. The predictive model can provide a reference for identifying cases with high risk of early death among stage IV breast cancer patients and play an auxiliary role in guiding individual treatment.

Keywords: Breast Neoplasms, nomograms, SEER Program



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