ReviewEuropean radiology2006
CAD for mammography: the technique, results, current role and further developments.
Review in European radiology, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed, 44 citations in OpenAlex.
- Improved computer-aided detection performance for pulmonary embolism on CTPA with higher noise reduction reconstruction.Emergency radiology · 2026Article
- A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions.Journal of personalized medicine · 2023Article
- Physical imaging parameter variation drives domain shift.Scientific reports · 2022Article
- ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
- Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network.PloS one · 2018Article
- Classifying symmetrical differences and temporal change for the detection of malignant masses in mammography using deep neural networks.Journal of medical imaging (Bellingham, Wash.) · 2017Article
- Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning.Scientific reports · 2016Article
- Role of computer-aided detection in very small screening detected invasive breast cancers.Journal of digital imaging · 2013Article
- Is computer aided detection (CAD) cost effective in screening mammography? A model based on the CADET II study.BMC health services research · 2011Article
- Mammographic features and histopathological findings of interval breast cancers.Acta radiologica (Stockholm, Sweden : 1987) · 2008Article
- [Workflow in digital screening mammography].Der Radiologe · 2008Review
- Variable size computer-aided detection prompts and mammography film reader decisions.Breast cancer research : BCR · 2008Article
- Digital mammography: what do we and what don't we know?European radiology · 2007Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
CAD systems, developed to assist the radiologist in the detection of suspicious lesions on mammograms, are currently controversially discussed. The highly sensitive detection of malignant structures including priors by CAD is linked with a low specific performance and a high rate of falsely positive markings. This causes controversial results regarding the effect of CAD systems for the diagnosing radiologist. This review aims to give an overview of the current literature, to state the currently discussed controversial results of CAD and to give an outlook on the next developments, which are not limited to senology, but include many other applications of CAD systems in radiology.
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.