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Objective This study aims to compare breast volume changes and other anthropometric measurements by using before and after breast reduction pictures of women who underwent breast reduction operation in Plastic and Reconstructive Surgery clinic and by performing measurements from the anatomic points indicated in the literature. Background Landmarks (previously identified as anatomic points) that show the success of breast reduction operation are not sufficient. Anthropometric points and their identification are of great importance for choosing the landmarks and identifying the statistical approaches to be used. Methods A total of 40 women were measured breast anthropometric measurements in pre- and post-operative breast reduction surgery changes by a photographic technique using Image J programme from the anatomical points determined in the literature. Comparison of right and left breast anthropometric measurements before and after the operation was performed using the paired t test or Wilcoxon signed rank test. The intraclass correlation coefficient (ICC) and Bland-Altman plots were used to determine the agreement between each pair of measurements. Results There was a statistically significant agreement between all the measurements (p<0.001). According to the Bland-Altman graphics, right and left breast measurements after the operation were within the limits of agreement according to all measurement points. Conclusion This study presented anthropometric measurements to show and guide patient satisfaction and aesthetic success of the operations performed by plastic surgeons.
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http://dx.doi.org/10.7759/cureus.4312 | DOI Listing |
Immunooncol Technol
September 2025
Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Background: Breast cancer is a systemic disease, yet the impact of tumor molecular subtype and disease stage on the systemic immune landscape remains poorly understood. In this study, we comprehensively analyzed the systemic immune landscape in a large cohort of breast cancer patients, encompassing all molecular subtypes and disease stages, alongside a control group of healthy donors.
Materials And Methods: Using multi-parameter flow cytometry, we assessed the abundance, phenotype, and activation status of diverse innate and adaptive immune cell populations across peripheral blood samples from 355 breast cancer patients and 65 healthy donors.
Radiat Res
September 2025
Division of Genetics and Epidemiology, Institute of Cancer Research, London, United Kingdom.
Contralateral breast (CB) cancer is the most common subsequent cancer among breast cancer survivors, and radiotherapy has been linked to CB cancer risk. The purpose of this work was to evaluate doses to subregions of the contralateral breast from historical breast cancer treatments carried out in the United States between 1990 and 2012. We extracted treatment data from radiation therapy summaries for 2,442 radiotherapy patients during that period.
View Article and Find Full Text PDFCell Death Dis
September 2025
Department of Endocrinology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, 510080, China.
In recent years, there has been a rapid increase in the incidence of thyroid carcinoma (TC). Our study focuses on the regulatory effect of circular RNAs on metabolism of TC, aiming to provide new insights into the mechanisms of progression and a potential therapeutic target for TC. In this study, we identified high expression levels of circPSD3 in TC tissues through RNA sequencing.
View Article and Find Full Text PDFEur J Radiol
August 2025
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA, USA.
Purpose: To evaluate whether AI-assisted ipsilateral tissue matching in digital breast tomosynthesis (DBT) reduces localization errors beyond typical tumor boundaries, particularly for non-expert radiologists. The technology category is deep learning.
Materials And Methods: The study consisted of two parts.
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
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