Accuracy of Digital Breast Tomosynthesis for Detecting Breast Cancer in the Diagnostic Setting: A Systematic Review and Meta-Analysis

Abstract

Objective: To compare the accuracy for detecting breast cancer in the diagnostic setting between the use of digital breast tomosynthesis (DBT), defined as DBT alone or combined DBT and digital mammography (DM), and the use of DM alone through a systematic review and meta-analysis.

Materials and methods: Ovid-MEDLINE, Ovid-Embase, Cochrane Library and five Korean local databases were searched for articles published until March 25, 2020. We selected studies that reported diagnostic accuracy in women who were recalled after screening or symptomatic. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to estimate pooled sensitivity and specificity. We compared the diagnostic accuracy between DBT and DM alone using meta-regression and subgroup analyses by modality of intervention, country, existence of calcifications, breast density, Breast Imaging Reporting and Data System category threshold, study design, protocol for participant sampling, sample size, reason for diagnostic examination, and number of readers who interpreted the studies.

Results: Twenty studies (n = 44513) that compared DBT and DM alone were included. The pooled sensitivity and specificity were 0.90 (95% confidence interval [CI] 0.86-0.93) and 0.90 (95% CI 0.84-0.94), respectively, for DBT, which were higher than 0.76 (95% CI 0.68-0.83) and 0.83 (95% CI 0.73-0.89), respectively, for DM alone (p < 0.001). The area under the summary receiver operating characteristics curve was 0.95 (95% CI 0.93-0.97) for DBT and 0.86 (95% CI 0.82-0.88) for DM alone. The higher sensitivity and specificity of DBT than DM alone were consistently noted in most subgroup and meta-regression analyses.

Conclusion: Use of DBT was more accurate than DM alone for the diagnosis of breast cancer. Women with clinical symptoms or abnormal screening findings could be more effectively evaluated for breast cancer using DBT, which has a superior diagnostic performance compared to DM alone.

Keywords: Breast cancer; Breast tomosynthesis; Mammography; Meta-analysis; Performance.

Fig. 1. Flow diagram of the study selection process.

Fig. 2. Risk of bias graph by the Quality Assessment of Diagnostic Accuracy Studies version 2, (A) risk of bias graph, and (B) risk of bias summary for each study.

Fig. 3. Coupled forest plots of the pooled sensitivity and specificity of (A) digital breast tomosynthesis and (B) digital mammography alone for breast cancer diagnosis.

The black square boxes denote either sensitivity (left panel) or specificity (right panel), and horizontal lines represent 95% CI for each study. The vertical dotted line indicates pooled summary estimates of sensitivity or specificity, and the diamond at the bottom indicates the 95% CIs. Heterogeneity statistics (I² value, Q value) for sensitivity and specificity are displayed. CI = confidence interval

Fig. 4. HSROC curves of (A) digital breast tomosynthesis and (B) digital mammography alone for breast cancer diagnosis.

HSROC curves show the individual (circles) and pooled (red square) sensitivity and specificity, and the dimension of each circle indicates the weight from the study sample size. There is a large difference between the 95% prediction (larger oval) and confidence region (small oval) in (B) digital mammography alone as well as (A) digital breast tomosynthesis, which suggests considerable heterogeneity among studies. HSROC = hierarchical summary receiver operating characteristic

Fig. 5. Deeks' funnel plot for digital breast tomosynthesis.

The p value of 0.24 for the slope coefficient indicates symmetry in the data and a low likelihood of publication bias. ESS = effective sample size