There are two common methods of obtaining high-quality screening mammography: screen-film mammography (more simply, mammography) using a dedicated unit, and xeromammography. We studied the accuracy of the two techniques in detecting breast cancer by a retrospective study, analysing accuracy of interpretation in cases where both mammography and xeromammography were performed. Seventy-six patients were considered with 86 biopsies and mammograms resulting in detection of 32 cancers and 54 benign lesions. There was no difference in accuracy of interpretation between mammography and xeromammography. The sensitivities were: mammography 0.91, xeromammography 0.88. The specificities were: mammography 0.63, xeromammography 0.75. We conclude that both modalities are comparable in detecting cancer when optimal technique and experienced personnel are used.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1259/0007-1285-59-708-1159 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Diversity, inclusivity and traceability of mammography datasets used in development of Artificial Intelligence technologies: a systematic review.
Clin Imaging
February 2025
University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK; Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK; National Institute for Health and Care Research Birmingham Biomedical Research Centre, University of Birmingham, Birmingham, UK; Birmingham Health Partners Centre for Regulatory Science and Innovation, University of Birmingham, Birmingham, UK. Electronic address:
Article Synopsis
- This review analyzes various mammography datasets used for AI development in breast cancer screening, focusing on their transparency, content, and accessibility.
- A search identified 254 datasets, with only 28 being accessible; most datasets came from Europe, East Asia, and North America, raising concerns over poor demographic representation.
- The findings highlight significant gaps in diversity within these datasets, underscoring the need for better documentation and inclusivity to enhance the effectiveness of AI technologies in breast cancer research.
Microcalcifications in benign breast biopsies: association with lesion type and risk.
Breast Cancer Res Treat
December 2024
Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA.
Purpose: To characterize associations of microcalcifications (calcs) with benign breast disease lesion subtypes and assess whether tissue calcs affect risks of ductal carcinoma in situ (DCIS) and invasive breast cancer (IBC).
Methods: We analyzed detailed histopathologic data for 4,819 BBD biopsies from a single institution cohort (2002-2013) followed for DCIS or IBC for a median of 7.4 years for cases (N = 338) and 11.
Revolutionizing Breast Cancer Detection With Artificial Intelligence (AI) in Radiology and Radiation Oncology: A Systematic Review.
Cureus
April 2024
Trauma Surgery, Order of St. Francis (OSF) St Francis Medical Centre, University of Illinois Chicago, Peoria, USA.
The number one cause of cancer in women worldwide is breast cancer. Over the last three decades, the use of traditional screen-film mammography has increased, but in recent years, digital mammography and 3D tomosynthesis have become standard procedures for breast cancer screening. With the advancement of technology, the interpretation of images using automated algorithms has become a subject of interest.
View Article and Find Full Text PDFDeep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors.
J Med Imaging (Bellingham)
January 2024
Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands.
Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.
Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.
Relationship between Race and Access to Newer Mammographic Technology in Women with Medicare Insurance.
Radiology
February 2023
From the Neiman Health Policy Institute, 1891 Preston White Dr, Reston, VA 20191 (E.W.C., M.W., E.Y.R.); Health Services Management, University of Minnesota, St Paul, Minn (E.W.C.); Department of Radiology, SUNY Downstate Health Sciences University, Brooklyn, NY (J.S.); Department of Radiology, Mayo Clinic Arizona, Phoenix, Ariz (B.K.P.); and Department of Radiology, Albert Einstein College of Medicine, Montefiore Medical Center, Bronx, NY (J.A.B.).
Background Racial disparities in breast cancer mortality have been reported. Mammographic technology has undergone two major technology transitions since 2000: first, the transition from screen-film mammography (SFM) to full-field digital mammography (FFDM) and second, the transition to digital breast tomosynthesis (DBT). Purpose To examine the relationship between use of newer mammographic technology and race in women receiving mammography services.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!