Diagnostic Performance of Breast Magnetic Resonance Imaging in Non-Calcified Equivocal Breast Findings: Results from a Systematic Review and Meta-Analysis

Abstract

Objectives: To evaluate the performance of MRI for diagnosis of breast cancer in non-calcified equivocal breast findings.

Materials and methods: We performed a systematic review and meta-analysis of peer-reviewed studies in PubMed from 01/01/1986 until 06/15/2015. Eligible were studies applying dynamic contrast-enhanced breast MRI as an adjunct to conventional imaging (mammography, ultrasound) to clarify equivocal findings without microcalcifications. Reference standard for MRI findings had to be established by histopathological sampling or imaging follow-up of at least 12 months. Number of true or false positives and negatives and other characteristics were extracted, and possible bias was determined using the QUADAS-2 applet. Statistical analyses included data pooling and heterogeneity testing.

Results: Fourteen out of 514 studies comprising 2,316 lesions met our inclusion criteria. Pooled diagnostic parameters were: sensitivity (99%, 95%-CI: 93-100%), specificity (89%, 95%-CI: 85-92%), PPV (56%, 95%-CI: 42-70%) and NPV (100%, 95%-CI: 99-100%). These estimates displayed significant heterogeneity (P<0.001).

Conclusions: Breast MRI demonstrates an excellent diagnostic performance in case of non-calcified equivocal breast findings detected in conventional imaging. However, considering the substantial heterogeneity with regard to prevalence of malignancy, problem solving criteria need to be better defined.

Fig 1. Flowchart depicting the selection process during systematic literature review.

Fig 2. Begg's funnel scatterplot analysis of selected studies.

The graph plots the logarithmic values of diagnostic odds ratios (logDOR) of considered studies in the abscissae axis against the standard error of logDOR in the ordinate axis. The two lines delimiting the inversed funnel denote pseudo 95% confidence intervals. Note the absence of any funnel plot asymmetry (confirmed by Egger´s testing).

Fig 3. Forest plots of sensitivity and specificity.

Sensitivity was defined as $\left(\frac{\text{tp}}{\text{tp}+\text{fn}}\right)$ and specificity as $\left(\frac{\text{tn}}{\text{tn}+\text{fp}}\right)$. tn: true negative; tp: true positive; fn: false negative; fp: false positive. All numbers have been rounded up or down to the closest second decimal.

Fig 4. Forest plots of the positive and negative predictive values.

The positive predictive value PPV was defined as $\left(\frac{\text{tp}}{\text{tp}+\text{fp}}\right)$ and the negative predictive value NPV as $\left(\frac{\text{tn}}{\text{tn}+\text{fn}}\right)$. tn: true negative; tp: true positive; fn: false negative; fp: false positive. All numbers have been rounded up or down to the closest second decimal.

Fig 5. Pre- and post-test probabilities.

A. Probability modifying plot. Note that post-test probabilities below 2% are achieved up to pre-test probabilities of 60%. B. Fagan´s Nomogram applying pooled positive (plain line) and negative (dashed line) likelihood ratios to a pretest probability of 25% (the 95% CI upper bond of pooled prevalence of malignancy in all selected studies). Resulting posttest probabilities were 60% and 0.15% for a positive or a negative MRI result, respectively.