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Background: Although breast cancer is rare in young women under 30 years of age, its incidence has been increasing among younger populations in recent years. Identifying the immunohistochemical molecular subtype of early-stage noninvasive breast cancer is thus crucial for optimizing therapeutic strategies. This study aimed to investigate the correlation between multimodal imaging features-including ultrasound (US), digital mammography (DM), and digital breast tomosynthesis (DBT)-and immunohistochemical molecular subtypes in young patients with breast cancer (≤30 years) and middle-aged patients with breast cancer (45-55 years).
Methods: A retrospective analysis included 146 young (≤30 years) and 292 middle-aged (45-55 years) patients with breast cancer diagnosed between January 2015 and March 2025. Imaging characteristics were assessed according to the Breast Imaging Report and Data System (BI-RADS) fifth edition. Mass lesions were evaluated for shape, margin, size, breast density, posterior features (on US), and vascularity (on US). Calcified lesions were analyzed for shape and distribution. Clinical and pathological parameters included age, symptoms, immunohistochemical classifications, T stage, histological grade, and lymph node metastasis status.
Results: The molecular subtype distribution in young and middle-aged patients, respectively, was as follows: luminal B, 70 and 140 cases; triple negative (TN), 32 and 64 cases; human epidermal growth factor receptor 2 (HER2) overexpression, 25 and 50 cases; and luminal A, 19 and 38 cases. (I) Younger patients demonstrated higher rates of nipple discharge (P=0.007), tumors >5.0 cm (P=0.009), stage T3 lesions (P<0.001), and lymph node metastasis (P<0.001), whereas middle-aged patients had higher rates of more palpable masses, tumors ≤2.0 cm, and stage T1 lesions. (II) US analysis indicated that luminal B tumors in younger patients had increased calcified masses (P=0.004), indistinct margins (P=0.004), and enhanced posterior features (P<0.001), which was in contrast to the noncalcified masses, spiculated margins, and shadowing found in middle-aged patients. TN tumors tended to be round/oval morphologies in younger patients (P=0.008). And TN tumors tended to appear as noncalcified masses in middle-aged patients (P=0.046). (III) DBT analysis indicated that younger patients with luminal B tumors were more likely to have isodense lesions (P=0.008) and microlobulated margins (P=0.003), while middle-aged patients tended to exhibit hyperdense lesions and indistinct margins; TN tumors in younger patients tended to have oval/round morphologies (P<0.001) and microlobulated margins (P=0.03); HER2-overexpressed tumors in younger patients tended to show isolated calcifications (P=0.02) and fine linear or branching calcifications, while HER2-overexpressed tumors in middle-aged patients were more likely to be pleomorphic or coarse calcifications (P=0.006).
Conclusions: Age-specific imaging patterns correlate with molecular subtypes and can predict aggressive subtypes in younger patients with breast cancer. These biomarkers may help guide preoperative planning and targeted therapy.
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http://dx.doi.org/10.21037/qims-2025-434 | 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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