Scientific Reports (Jul 2024)

Epidemiology and prognostic nomogram for invasive breast cancer aged 85 years and older in the USA

  • Xiu Yang,
  • Qian Wu,
  • Jie Jian,
  • Yanlin Qu,
  • Yating Qiang,
  • Xumei Li,
  • Qingsong Peng

DOI
https://doi.org/10.1038/s41598-024-67527-3
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 10

Abstract

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Abstract The available data on epidemiology and prognostic factors of female patients with breast cancer aged 85 years and older in the USA are limited, especially regarding molecular-level heterogeneity. Relevant data were extracted from the surveillance, epidemiology, and end-result database. The incidence rate and the annual prevalence rate were determined. The annual percent change (APC) of incidence was measured to determine the gradual trends or changes in rates. A visual nomogram was constructed to predict the 3-year overall survival (OS). The Kaplan–Meier method and log-rank test were performed for survival analysis. In total, 18,137 female patients with invasive breast cancer aged 85 years and older were included. Among these patients, patients with HR+/HER2− accounted for 68.7%, followed by HR−/HER2− (9.3%), HR+/HER2+ (7.4%), and HR−/HER2+ (3.1%). The overall incidence rate among this population was 181.82 (95% CI 179.18–184.49) per 100,000 women. This decreased from 184.73 to 177.71 per 100,000 women from 2010 to 2019, with an APC of − 1.0 (95% CI − 1.8 to − 0.1, P = 0.036). The incidence rate varied across receptor subtypes and races and was higher in patients with HR+/HER2− or the black population. The most common treatment regime was breast-conserving surgery. Approximately 29.2% of all patients were categorized as receiving no treatment. A nomogram for predicting 3-year overall survival was constructed, with a consistency index of 0.71. Furthermore, the calibration curves showed consistency. In this study, we have presented the epidemiological data of invasive breast cancer in females aged 85 years and older in the USA. The developed predictive nomogram can effectively identify patients with poor survival.

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