ArticleEuropean radiology2019
A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- Machine Learning Based on Digital Mammography to Reduce the Need for Invasive Biopsies of Benign Calcifications Classified in BI-RADS Category 4.Journal of imaging informatics in medicine · 2025Article
- Feasibility of personalized screening and prevention recommendations in the general population through breast cancer risk assessment: results from a dedicated risk clinic.Breast cancer research and treatment · 2022Article
- Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.Frontiers in oncology · 2022Article
- Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk.Scientific reports · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesRadiologists' visual assessment of breast mammographic density (BMD) is subject to inter-observer variability. We aimed to develop and validate a new automated software tool mimicking expert radiologists' consensus assessments of 2D BMD, as per BI-RADS V recommendations.
methodsThe software algorithm was developed using a concept of Manhattan distance to compare a patient's mammographic image to reference mammograms with an assigned BMD category. Reference databases were built from a total of 2289 pairs (cranio-caudal and medio-lateral oblique views) of 2D full-field digital mammography (FFDM). Each image was independently assessed for BMD by a consensus of radiologists specialized in breast imaging. A validation set of additional 800 image pairs was evaluated for BMD both by the software and seven blinded radiologists specialized in breast imaging. The median score was used for consensus. Software reproducibility was assessed using FFDM image pairs from 214 patients in the validation set to compare BMD assessment between left and right breasts.
resultsThe software showed a substantial agreement with the radiologists' consensus (unweighted κ = 0.68, 95% CI 0.64-0.72) when considering the four breast density categories, and an almost perfect agreement (unweighted κ = 0.84, 95% CI 0.80-0.88) when considering clinically significant non-dense (A-B) and dense (C-D) categories. Correlation between left and right breasts was high (r
conclusionsBMD assessment by the software was strongly correlated to radiologists' consensus assessments of BMD. Its performance should be compared to other methods, and its clinical utility evaluated in a risk assessment model. KEY POINTS: • A new software tool assesses breast density in a standardized way. • The tool mimics radiologists' clinical assessment of breast density. • It may be incorporated in a breast cancer risk assessment model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.