PET/CT and MRI for Identifying Axillary Lymph Node Metastases in Breast Cancer Patients: Systematic Review and Meta-Analysis.

Abstract

Axillary lymph node metastases (ALNM) is one of the most important prognostic factors in breast cancer. Positron emission tomography / computed tomography (PET/CT) and magnetic resonance imaging (MRI) are increasingly used to assess ALNM noninvasively. There has been no study investigating PET/CT and MRI in direct comparative studies. To assess the diagnostic accuracy of PET/CT and MRI for ALNM in the same population of breast cancer. Systematic review and meta-analysis. PubMed-MEDLINE, Web of Science, Cochrane, EMBASE, and Chinese Biomedical Literature databases were searched (September 1993-January 2020) by using concerned keywords. Studies using both MRI and PET/CT as diagnostic methods were included. Eleven studies included 1203 breast cancer patients using PET/CT and 1186 patients using MRI. 1.5T or 3.0T. The Quality Assessment of Diagnostic Accuracy Studies (v. 2) was used to assess the quality of the studies. A bivariate mixed-effects binary regression model was used to obtain the diagnostic performance. Meta-regression analysis was conducted to investigate study heterogeneity. A total of 369 articles were screened; out of these, 11 studies were included that meet the inclusion criteria. The respective pooled sensitivity and specificity values were 0.56 (95% confidence interval [CI]: 0.47-0.63) and 0.91 (95% CI: 0.87-0.93) for PET/CT and 0.55 (95% CI: 0.48-0.62) and 0.86 (95% CI: 0.82-0.89) for MRI. There was no statistically significant difference in sensitivity (P = 0.769) or specificity (P = 0.447) between PET/CT and MRI. There was no threshold effect in either of the imaging tests. The diagnostic performance of both imaging tests was affected by study design, breast cancer subtype, tumor stage, or imaging features. In the same population, PET/CT and MRI had comparable diagnostic performance for the detection of ALNM, with low sensitivity and high specificity. 3. Stage 2. J. MAGN. RESON. IMAGING 2020;52:1840-1851.