ObjectiveTo evaluate the values of diffusion weighted imaging (DWI) sequence in the diagnosis of node metastases in breast cancer by meta-analysis. MethodsThe articles concerning the diagnosis of node metastases by using DWI until September 2016 were searched in databases including The Cochrane Library, PubMed, EMbase, Web of Science, CBM, VIP, WanFang Data and CNKI. Two reviewers independently screened literature, extracted data, and assessed the risk of bias of included studies by using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2) tool. Then, meta-analysis was performed by using Stata 12.0 software. The pooled weighted sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated, the summary receiver operating characteristic curve (SROC) was drawn and the area under the curve was calculated. ResultsA total of 21 articles were included, involving 25 studies. The results of meta-analysis showed that, the pooled sensitivity, specificity, DOR and area under SROC curve of DWI for diagnosing node metastases were 0.85 (95%CI 0.80 to 0.89), 0.83 (95%CI 0.78 to 0.87), 4.99 (95%CI 3.74 to 6.67), 0.18 (95%CI 0.13 to 0.24), 3.32 (95%CI 2.82 to 3.82), and 0.91 (95%CI 0.88 to 0.93), respectively. The results of subgroup analysis showed that DWI had better Spe in b value=750-1 000 than b value=400-600; The 1.5T DWI had better Sen and Spe in diagnosing node metastases compared with 1.5T DWI. ConclusionDWI has more diagnostic efficiency for assessing lymph node metastases, especially in b value=750-1 000 and 1.5T field MR syetem. Due to limited quantity and quality of the included studies, more high-quality studies are required to verify the above conclusion.
Among numerous medical imaging modalities, diffusion weighted imaging (DWI) is extremely sensitive to acute ischemic stroke lesions, especially small infarcts. However, magnetic resonance imaging is time-consuming and expensive, and it is also prone to interference from metal implants. Therefore, the aim of this study is to design a medical image synthesis method based on generative adversarial network, Stroke-p2pHD, for synthesizing DWI images from computed tomography (CT). Stroke-p2pHD consisted of a generator that effectively fused local image features and global context information (Global_to_Local) and a multi-scale discriminator (M2Dis). Specifically, in the Global_to_Local generator, a fully convolutional Transformer (FCT) and a local attention module (LAM) were integrated to achieve the synthesis of detailed information such as textures and lesions in DWI images. In the M2Dis discriminator, a multi-scale convolutional network was adopted to perform the discrimination function of the input images. Meanwhile, an optimization balance with the Global_to_Local generator was ensured and the consistency of features in each layer of the M2Dis discriminator was constrained. In this study, the public Acute Ischemic Stroke Dataset (AISD) and the acute cerebral infarction dataset from Yantaishan Hospital were used to verify the performance of the Stroke-p2pHD model in synthesizing DWI based on CT. Compared with other methods, the Stroke-p2pHD model showed excellent quantitative results (mean-square error = 0.008, peak signal-to-noise ratio = 23.766, structural similarity = 0.743). At the same time, relevant experimental analyses such as computational efficiency verify that the Stroke-p2pHD model has great potential for clinical applications.