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Background: Identifying risk factors for aggressive forms of breast cancer is important. Tumor factors (e.g., stage) are important predictors of prognosis, but may be intermediates between prediagnosis risk factors and mortality. Typically, separate models are fit for incidence and mortality postdiagnosis. These models have not been previously integrated to identify risk factors for lethal breast cancer in cancer-free women.
Methods: We combined models for breast cancer incidence and breast cancer-specific mortality among cases into a multi-state survival model for lethal breast cancer. We derived the model from cancer-free postmenopausal Nurses' Health Study women in 1990 using baseline risk factors. A total of 4,391 invasive breast cancer cases were diagnosed from 1990 to 2014 of which 549 died because of breast cancer over the same period.
Results: Some established risk factors (e.g., family history, estrogen plus progestin therapy) were not associated with lethal breast cancer. Controlling for age, the strongest risk factors for lethal breast cancer were weight gain since age 18: > 30 kg versus ± 5 kg, RR = 1.94 [95% confidence interval (CI) = 1.38-2.74], nulliparity versus age at first birth (AAFB) < 25, RR = 1.60 (95% CI = 1.16-2.22), and current smoking ≥ 15 cigarettes/day versus never, RR = 1.42 (95% CI = 1.07-1.89).
Conclusions: Some breast cancer incidence risk factors are not associated with lethal breast cancer; other risk factors for lethal breast cancer are not associated with disease incidence.
Impact: This multi-state survival model may be useful for identifying prediagnosis factors that lead to more aggressive and ultimately lethal breast cancer.
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http://dx.doi.org/10.1158/1055-9965.EPI-21-1471 | DOI Listing |
EBioMedicine
September 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China. Electronic address:
JMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDFEpidemiol Serv Saude
September 2025
Universidade Estadual do Norte do Paraná, Programa de Pós-Graduação em Enfermagem em Atenção Primária à Saúde Bandeirantes, PR, Brazil.
Objectives: To analyze the temporal trend and identify spatial clusters of breast cancer mortality in Paraná state between 2012 and 2021.
Methods: This was a time series study, with spatial analysis of breast cancer mortality rates in the 399 municipalities of Paraná. Data were selected from the Mortality Information System.
Cien Saude Colet
August 2025
Faculdade de Medicina da Universidade Federal de Pelotas. Pelotas RS Brasil.
The objective of this study was to analyze the characteristics of avoidable mortality in the population aged five to 69 years living in the city of Pelotas/RS, comparing it with the rest of the state of Rio Grande do Sul, from 2000 to 2021. An ecological study was conducted analyzing avoidable mortality coefficients according to sex and age, from 2000 to 2021. The data source was the Mortality Information System, and the trend analysis was performed using Prais-Winsten regression, with standardization of coefficients.
View Article and Find Full Text PDFCien Saude Colet
August 2025
Programa de Pós-Graduação em Nutrição e Saúde, Universidade Estadual do Ceará. R. Betel 1958, Itaperi. 60714-230 Fortaleza CE Brasil.
This study aimed to evaluate mortality due to female breast cancer attributable to overweight and obesity and to estimate the number of preventable deaths with a reduction in the Body Mass Index in Brazil. An ecological study was carried out with investigation of information on overweight, obesity, sociodemographic characteristics based on a national survey carried out in 2013-14; breast cancer mortality rate in 2019 using the Online Atlas of Mortality and Relative Risk Meta-Analyses. The Potential Impact Fraction analysis was carried out, considering the following counterfactual scenarios related to the reduction in BMI: Scenario A - population contingent of women that make up the prevalence of overweight and obesity now composes the prevalence of eutrophy; Scenario B - population contingent of women that make up the prevalence of overweight starts to make up the prevalence of eutrophy; Scenario C - population contingent of women that make up the prevalence of obesity becomes part of the prevalence of overweight.
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