Objective: We assessed and compared the benefit of using images acquired 1 year or 2 years previously during mammography interpretations.

Materials And Methods: Eleven radiologists and one resident reviewed 128 cases three times: once without prior mammograms for comparison, once with mammograms from the most recent (1 year) examination, and once with mammograms acquired 2 years previously. They were asked to determine whether the patient should be recalled for additional procedures. Performances under the three conditions were compared.

Results: Radiologists were significantly more accurate (p < 0.001) when comparison mammograms (obtained 1 or 2 years previously) were available. Although sensitivity was not significantly affected between the availability of mammograms from 1 or 2 years earlier (p > 0.10), the specificity was. Specificity using mammograms from the latest examination (obtained 1 year previously) as a reference was significantly better (p = 0.03) than specificity using mammograms obtained 2 years previously.

Conclusion: Comparison mammograms are important for accurate diagnosis-in particular, for increasing specificity. The latest prior examination seems to be the optimal one for this purpose.

Download full-text PDF

Source
http://dx.doi.org/10.2214/ajr.180.2.1800343DOI Listing

Publication Analysis

Top Keywords

comparison mammograms
16
mammograms years
16
mammograms
9
specificity mammograms
8
years
6
optimal reference
4
reference mammography
4
comparison
4
mammography comparison
4
examination
4

Similar Publications

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

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.

View Article and Find Full Text PDF

Conformable Ultrasound Breast Patch - The Future of Breast Cancer Screening?

Eur J Breast Health

January 2025

Division of Gastrointestinal and Oncologic Surgery, Department of Surgery, Massachusetts General Hospital, Boston, USA.

Breast cancer is the most common cancer type among women worldwide with an average lifetime risk of 12.9%. Early detection and screening are the most important factors for improved prognosis and mammography remains the main screening tool for the average risk patients.

View Article and Find Full Text PDF

Fourier analysis of signal dependent noise images.

Sci Rep

December 2024

Cancer Epidemiology Department, H. Lee Moffitt Cancer Center and Research Institute, 12902 Bruce B. Downs Blvd, Tampa, FL, 33612, USA.

An archetype signal dependent noise (SDN) model is a component used in analyzing images or signals acquired from different technologies. This model-component may share properties with stationary normal white noise (WN). Measurements from WN images were used as standards for making comparisons with SDN in both the image domain (ID) and Fourier domain (FD).

View Article and Find Full Text PDF

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!