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1.
Breast Cancer Res Treat ; 204(2): 193-222, 2024 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-38100015

RESUMO

PURPOSE: To examine the current evidence on breast lymphedema (BL) diagnosis and treatment after breast-conserving surgery, identify gaps in the literature, and propose future research directions. METHODS: A comprehensive literature review was conducted using Ovid, PubMed, and Cochrane, including studies published between 2000 and 2023. References were reviewed manually for eligible studies. Inclusion criteria were as follows: patients who underwent breast conserving treatment (surgery ± radiation) for breast cancer, goals of the paper included analyzing or reviewing BL measurement with ultrasound or tissue dielectric constant, or BL treatment. Twenty-seven manuscripts were included in the review. RESULTS: There is variation in incidence, time course, and risk factors for BL. Risk factors for BL included breast size, primary and axillary surgery extent, radiation, and chemotherapy but require further investigation. Diagnostic methods for BL currently rely on patient report and lack standardized criteria. Tissue dielectric constant (TDC) and ultrasound (US) emerged as promising ambulatory BL assessment tools; however, diagnostic thresholds and validation studies with ICG lymphography are needed to establish clinical utility. The evidence base for treatment of BL is weak, lacking high-quality studies. CONCLUSION: The natural history of BL is not well defined. TDC and US show promise as ambulatory assessment tools for BL; however, further validation with lymphatic imaging is required. BL treatment is not established in the literature. Longitudinal, prospective studies including pre-radiation measurements and validating with lymphatic imaging are required. These data will inform screening, diagnostic criteria, and evidence-based treatment parameters for patients with BL after breast-conserving surgery and radiation.


Assuntos
Linfedema Relacionado a Câncer de Mama , Neoplasias da Mama , Linfedema , Humanos , Feminino , Neoplasias da Mama/cirurgia , Neoplasias da Mama/complicações , Estudos Prospectivos , Linfedema/diagnóstico , Linfedema/epidemiologia , Linfedema/etiologia , Mastectomia Segmentar/efeitos adversos , Axila , Linfedema Relacionado a Câncer de Mama/diagnóstico , Linfedema Relacionado a Câncer de Mama/epidemiologia , Linfedema Relacionado a Câncer de Mama/etiologia
2.
Acad Radiol ; 29(6): 841-850, 2022 06.
Artigo em Inglês | MEDLINE | ID: mdl-34563442

RESUMO

RATIONALE AND OBJECTIVES: To quantitatively compare breast parenchymal texture between two Digital Breast Tomosynthesis (DBT) vendors using images from the same patients. MATERIALS AND METHODS: This retrospective study included consecutive patients who had normal screening DBT exams performed in January 2018 from GE and normal screening DBT exams in adjacent years from Hologic. Power spectrum analysis was performed within the breast tissue region. The slope of a linear function between log-frequency and log-power, ß, was derived as a quantitative measure of breast texture and compared within and across vendors along with secondary parameters (laterality, view, year, image format, and breast density) with correlation tests and t-tests. RESULTS: A total of 24,339 DBT slices or synthetic 2D images from 85 exams in 25 women were analyzed. Strong power-law behavior was verified from all images. Values of ß d did not differ significantly for laterality, view, or year. Significant differences of ß were observed across vendors for DBT images (Hologic: 3.4±0.2 vs GE: 3.1±0.2, 95% CI on difference: 0.27 to 0.30) and synthetic 2D images (Hologic: 2.7±0.3 vs GE: 3.0±0.2, 95% CI on difference: -0.36 to -0.27), and density groups with each vendor: scattered (GE: 3.0±0.3, Hologic: 3.3±0.3) vs. heterogeneous (GE: 3.2±0.2, Hologic: 3.4±0.1), 95% CI (-0.27, -0.08) and (-0.21, -0.05), respectively. CONCLUSION: There are quantitative differences in the presentation of breast imaging texture between DBT vendors and across breast density categories. Our findings have relevance and importance for development and optimization of AI algorithms related to breast density assessment and cancer detection.


Assuntos
Neoplasias da Mama , Mamografia , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Feminino , Humanos , Masculino , Mamografia/métodos , Programas de Rastreamento , Estudos Retrospectivos , Análise Espectral
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