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Background: The estrogen receptor (ER) is one of the key biomarkers in breast cancer (BC), and therapy decisions are based on ER expression levels. However, the benefit of endocrine therapy in patients with ER expression (ER) is debatable. Owing to aggressive tumor biology, like triple-negative BC patients, many ER patients are considered to have worse outcomes and may benefit from additional drugs. This treatment dilemma in ER patients can be addressed by prognostication for risk of recurrence, which remains underexplored.
Objective: The study aims to assess whether CanAssist Breast (CAB), an immunohistochemistry-based prognostic test validated globally in ER+/PR+/HER2- early-stage breast cancer (EBC) patients, would help prognosticate ER patients and thereby aid in treatment planning.
Design: We conducted secondary data analyses of previously published retrospective studies to evaluate CAB prognostication in ER and ER subgroups across different clinical parameters.
Methods: Analysis of CAB-based risk stratification was conducted on 2896 ER+/PR+/HER2- EBC patients with a known percentage of ER staining in both ER and ER subgroups. Kaplan-Meier survival curves were used to evaluate distant recurrence-free interval (DRFI).
Results: ER patients constituted 6% of the total cohort. Overall, CAB significantly identified 65% of ER patients as low risk (LR) with acceptable DRFI of 91% and 35% as high risk (HR) with worse DRFI of 61% ( < 0.0001; hazard ratio (HR/LR), 5.175). ER patients are mostly younger, with T2, grade 3, lymph node positive tumors, and have a twofold higher incidence of distant recurrence than ER patients. CAB-based prognostication was significant in these subgroups analyzed with acceptable DRFI in LR patients of ~90% and a drop in DRFI in HR patients to ⩽66% ( = 0.01 to < 0.0001).
Conclusion: CAB-based risk stratification of ER patients is significant and would add value in treatment decisions for additional targeted treatments to HR patients.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12104605 | PMC |
http://dx.doi.org/10.1177/17588359251342218 | 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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