Evidence map›Paper›PMID 24121712›Full record

SynthesisEuropean radiology2014

Diagnostic accuracy of digital breast tomosynthesis versus digital mammography for benign and malignant lesions in breasts: a meta-analysis.

Junqiang Lei, Pin Yang, Li Zhang, Yinzhong Wang, Kehu Yang

Abstract readComparative StudyMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in European radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

37 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Junqiang Lei
Pin Yang
Li Zhang
Yinzhong Wang
Kehu Yang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the diagnostic performance of digital breast tomosynthesis (DBT) and digital mammography (DM) for benign and malignant lesions in breasts.

methodsDocument retrieval was conducted on PubMed, EMBASE, the Cochrane Library, Web of Science and Chinese Biomedical Literature Database, etc., from 1950 to June 2013. Metadisc1.4 software was used to analyse the pooled sensitivity, specificity, diagnostic odds ratio (DOR), and positive and negative likelihood ratio. The heterogeneity was assessed using forest plots and the inconsistency index (I(2)). Before statistical comparison, the area under (AUC) the summary receiver-operating characteristic curve (SROC) of two different diagnostic methods was calculated respectively.

resultsA total of seven studies involving 2,014 patients and 2,666 breast lesions were included. Compared with the gold standard (histological results), the pooled sensitivity and specificity of DBT were 90.0% and 79.0%, and for DM they were 89.0% and 72.0%, respectively. The pooled positive likelihood ratio of DBT and DM was 3.50 and 2.83; the pooled negative likelihood ratio of DBT and DM was 15% and 18%; the pooled DOR for DBT and DM was 26.04 and 16.24, respectively.

conclusionsDigital breast tomosynthesis has a higher sensitivity and specificity in breast diagnosis than digital mammography. KEY POINTS: • Digital breast tomosynthesis has high sensitivity and specificity in breast diagnosis.• DBT appears to have superior diagnostic accuracy relative to digital mammography. • DBT images were captured at a lower dose than 2D images. • DBT displays abnormal features of lesions more clearly than DM. • Digital breast tomosynthesis could become the first choice for assessing breast lesions.

Indexed as

Breast NeoplasmsFemaleHumansMammographyNeoplasmsRadiographic Image EnhancementReproducibility of ResultsRisk AssessmentROC CurveSensitivity and SpecificityTomography, X-Ray Computed

Identifiers

What OpenQuestion holds

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Registered trials

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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.