Regular screening mammograms for asymptomatic women are the most effective method for early detection of breast cancer. This study assessed the relative influence of Health Belief Model (HBM) constructs on prior mammography usage and the intention to obtain mammograms with data from a sample of 1,057 women over the age of 35 years residing in an urban community in the United States. Covariance structure analysis with latent variables was used initially to perform a confirmatory factor analysis of indicators of Socioeconomic Status (SES), Perceived Susceptibility, Perceived Barriers, Perceived Benefits, Cues to Action, Prior Mammography, and Future Intentions. Once a plausible factor structure was confirmed, a predictive path model was tested with Future Intentions and Prior Mammography as the outcome variables. Cues to Action, operationalized as a physician influence variable, particularly impacted Prior Mammography, and Perceived Susceptibility was the most powerful predictor of Future Intentions. SES only related significantly to Perceived Barriers, and Cues to Action, and did not directly influence Prior Mammography and Future Intentions. HBM predictor variables alone accounted for the relationship between previous mammography experience and intentions to obtain mammograms in the future. Health education implications and an applied outreach program are discussed.
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http://dx.doi.org/10.1177/109019819201900409 | DOI Listing |
J Med Imaging (Bellingham)
January 2025
Lund University, Department of Translational Medicine, Medical Radiation Physics, Malmö, Sweden.
Purpose: We aim to investigate the characteristics and evaluate the performance of synthetic mammograms (SMs) based on wide-angle digital breast tomosynthesis (DBT) compared with digital mammography (DM).
Approach: Fifty cases with both synthetic and digital mammograms were selected from the Malmö Breast Tomosynthesis Screening Trial. They were categorized into five groups consisting of normal cases and recalled cases with false-positive and true-positive findings from DM and DBT only.
Eur Radiol
January 2025
Department of Radiology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Objectives: To estimate tumour volume doubling time (TVDT) of interval cancers (ICs).
Methods: Two radiologists retrospectively reviewed prior screening and diagnostic mammograms and measured mean diameter on "visible" ICs. Univariate analyses of clinicopathological variables (ER, HER2, grade, age at diagnosis, and breast density) were undertaken, and those with p < 0.
Eur J Radiol Open
June 2025
Radiology Department, National Cancer Institute, Cairo University, Egypt.
Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Methods And Materials: Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications.
Appl Radiat Isot
January 2025
School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou, 325035, China. Electronic address:
For the purpose of assessing image quality and calculating patient X-ray dosage in radiology, computed tomography (CT), fluoroscopy, mammography, and other fields, it is necessary to have prior knowledge of the X-ray energy spectrum. The main components of an X-ray tube are an electron filament, also known as the cathode, and an anode, which is often made of tungsten or rubidium and angled at a certain angle. At the point where the electrons generated by the cathode and the anode make contact, a spectrum of X-rays with energies spanning from zero to the maximum energy value of the released electrons is created.
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.
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