Diagnostic accuracy of digital breast tomosynthesis versus digital mammography for benign and malignant lesions in breasts: a meta-analysis
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To evaluate the diagnostic performance of digital breast tomosynthesis (DBT) and digital mammography (DM) for benign and malignant lesions in breasts.
Document 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 (I2). Before statistical comparison, the area under (AUC) the summary receiver-operating characteristic curve (SROC) of two different diagnostic methods was calculated respectively.
A 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.
Digital breast tomosynthesis has a higher sensitivity and specificity in breast diagnosis than digital mammography.
• 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.
KeywordsDigital breast tomosynthesis Mammography Breast neoplasms Diagnosis Meta-analysis
Digital breast tomosynthesis
Full field digital mammography
Diagnostic odds ratio
Summary receiver-operating characteristic curve
Area under the curve
The authors would like to thank Prof. Jinhui Tian from the Evidence-based Medicine Centre, School of Basic Medical Sciences, Lanzhou University, for his help with the meta-analysis approach and some related statistical software for radiological diagnosis applications used in this study. The other authors have no conflict of interest to disclose.
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