The Diagnosis of Metastatic Axillary Lymph Nodes of Breast Cancer By Diffusion Weighted Imaging: A meta-analysis and systematic review

Abstract

Background: The purpose of this meta-analysis was to evaluate the clinical significance of diffusion-weighted imaging in assessing the status of axillary lymph nodes in patients with breast cancer. Methods: We searched the PubMed, Cochrane, and EMBASE databases, selected studies by inclusion and exclusion criteria, and assessed the quality of selected studies. We explored the source of heterogeneity; calculated sensitivity, specificity, positive and negative likelihood ratios, and pretest probability. A summary receiver operating characteristic curve was performed. Student's t test was used to compare the different mean apparent diffusion coefficient values of different status lymph nodes. Results: In selected 10 studies, a total of 801 patients and 2305 lymph nodes were included following inclusion criteria. All scores of the quality assessment of the included studies were greater than or equal to 10 points. The sensitivity was 0.89 (95 % CI 0.79-0.95), the specificity was 0.83 (95 % CI 0.71-0.91), the positive and negative likelihood ratios were 3.86 (95 % CI 2.75-5.41) and 0.17 (95 % CI 0.09-0.32), the pretest probabilities were 53 and 54 %, the area under the curve were 0.93 (95 % CI 0.90-0.95), respectively. The mean apparent diffusion coefficient value of metastatic lymph nodes was significantly lower than that of nonmetastatic axillary lymph nodes. Conclusions: Diffusion-weighted imaging is a promising tool to discriminate between metastatic and nonmetastatic axillary lymph nodes. Combined with the mean apparent diffusion coefficient value, it can quantitatively diagnose lymph node metastases. Conducting large-scale, high-quality researches can improve the clinical significance of diffusion-weighted imaging to distinguish metastatic and nonmetastatic axillary lymph nodes in patients with breast cancer and provide the evidence to assess the status of axillary lymph nodes.