Mammography is currently the only screening method available with proven capability to diagnose nonpalpable breast cancer. However, mammography does not detect all cancers. Cancers are more difficult to detect in radiographically dense breasts because lesions are obscured by breast tissue. Digital mammography and magnetic resonance imaging of the breast may minimize the problems associated with screening dense breasts. If these methods prove to be useful, more cancers could be detected at an earlier stage.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/BF00694742 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Annual Screening Mammography as a Standard for All Women: Counterpoint-Equity in Screening Outcome Requires Inequality in Screening Approach.
AJR Am J Roentgenol
January 2025
Department of Surgery, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
Annual Screening Mammography as a Standard for All Women Starting at Age 40: Point-Greatest Numbers of Lives Saved and Life-Years Saved, With Ongoing Advances to Mitigate Potential Harms.
AJR Am J Roentgenol
January 2025
UPMC Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA 15213.
Digital Mammography, Tomosynthesis, and Contrast-Enhanced Mammography: Intraindividual Comparison of Mean Glandular Dose for Screening Examinations.
AJR Am J Roentgenol
January 2025
Department of Radiology, Division of Breast Imaging and Intervention, Mayo Clinic, Phoenix, AZ.
Contrast-enhanced mammography (CEM) is growing in clinical use due to its increased sensitivity and specificity compared to full-field digital mammography (FFDM) and/or digital breast tomosynthesis (DBT), particularly in patients with dense breasts. To perform an intraindividual comparison of MGD between FFDM, DBT, a combination protocol using both FFDM and DBT (combined FFDM-DBT), and CEM, in patients undergoing breast cancer screening. This retrospective study included 389 women (median age, 57.
View Article and Find Full Text PDFAttention-guided erasing for enhanced transfer learning in breast abnormality classification.
Int J Comput Assist Radiol Surg
January 2025
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Bayern, Germany.
Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.
Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.
Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence.
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.
Want 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!