Background: Factors that may hamper access to mammographic screening in any given region include socioeconomic limitations and the geographical distribution and quality of the mammography machines. This study evaluated access to breast cancer screening within the Brazilian National Health Service (SUS), the geographical distribution of mammography equipment and the number of mammograms performed in Brazil. Methods: This ecological study evaluated the availability of mammography machines within the SUS, those available for Brazil as a whole, its macroregions, states and the Federal District in 2016. The number of mammography machines required for breast cancer screening was calculated and compared to the number of machines available. The expected number of mammograms was compared with the actual number performed. Machines were georeferenced based on their location and the municipal seat, according to healthcare region, with 60 km being defined as the maximum distance for an individual to travel for a mammogram. Results: In 2016, there were 4,628 mammography machines in Brazil. Of these, 4,492 were in use and 2,113 (47%) were available to the SUS. Considering the number of mammograms required as a function of the number clinically indicated, 2,068 machines would be required for breast cancer screening in Brazil. The network of machines available would be capable of producing 14,279,654 exams; however, only 4,073,079 exams were performed, representing 29% of the total capacity of production in the country in 2016. Regarding the maximum distance of 60 km to access a mammogram, only relatively small areas of Brazil were found not to meet this indicator. Conclusion: These results suggest that the difficulty of the Brazilian population in accessing breast cancer screening through the SUS is not associated with the number of machines available or with the geographical location of the equipment but rather with the insufficient number of mammograms performed.
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http://dx.doi.org/10.31557/APJCP.2019.20.6.1857 | DOI Listing |
Am J Cancer Res
December 2024
Department of Breast Surgery, Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine Shanghai 200011, China.
Breast cancer is one of the malignant tumors that seriously threaten women's health, and early diagnosis and detection of breast cancer are crucial for effective treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an important diagnostic tool that allows for the dynamic observation of blood flow characteristics of breast tumors, including small lesions within the affected tissue. Currently, it is widely used in clinical practice and has been shown promising prospects.
View Article and Find Full Text PDFAcad Radiol
January 2025
Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA 15213 (C.L., S.W.); Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213 (D.A., M.Z., J.S., S.W.); Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15213 (S.W.); Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA 15213 (S.W.). Electronic address:
Rationale And Objectives: In the USA over 1 million breast biopsies are performed annually. Approximately 9.6% diagnostic exams were given Breast Imaging Reporting and Data System (BI-RADS) ≥4A, most of which are 4A/4B.
View Article and Find Full Text PDFEur Radiol
January 2025
Department of Information Technology, Uppsala University, 75237, Uppsala, Sweden.
Objectives: The aim is to assess the feasibility and accuracy of a novel quantitative ultrasound (US) method based on global speed-of-sound (g-SoS) measurement using conventional US machines, for breast density assessment in comparison to mammographic ACR (m-ACR) categories.
Materials And Methods: In a prospective study, g-SoS was assessed in the upper-outer breast quadrant of 100 women, with 92 of them also having m-ACR assessed by two radiologists across the entire breast. For g-SoS, ultrasonic waves were transmitted from varying transducer locations and the image misalignments between these were then related analytically to breast SoS.
Breast Cancer Res Treat
January 2025
Massachusetts General Hospital, 55 Fruit St, WAC 240, Boston, MA, 02114, USA.
Purpose: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to compare the performance of a traditional machine learning CADe algorithm for synthetic 2D mammography to a deep learning-based AI algorithm for DBT on the same mammograms.
Methods: Mammographic examinations from 764 patients (mean age 58 years ± 11) with 106 biopsy-proven cancers and 658 cancer-negative cases were analyzed by a CADe algorithm (ImageChecker v10.
J Med Imaging (Bellingham)
January 2025
The University of Tokyo Hospital, Department of Radiology, Tokyo, Japan.
Purpose: The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance.
Approach: We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods.
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