RESUMEN
Background: To systematically evaluate the qualitative diagnostic value of multimodal magnetic resonance imaging (MRI) for parotid gland tumors. However, there is still a lot of controversy in this area, and the results of different studies are not consistent. Therefore, it is necessary to use meta method to analyze the significance of multimodal MRI in the diagnosis of parotid gland tumors. Methods: This study aimed to assess the diagnostic performance of multimodal MRI for parotid gland malignancies. We performed a search in the databases of the Cochrane Library, PubMed, Web of Science, Embase, Chinese BioMedical Literature (CBM). Quality evaluation and data extraction were performed for the included articles, and meta-analysis was performed on the included studies using Stata 15.0 software. Results: After screening, a total of 5 relevant documents met the standards and were included. The results of analysis showed that the MRI with diffusion-weighted imaging (MRI-DWI) combined sensitivity and specificity were 0.54 (0.22-0.83) and 0.93 (0.79-0.98). The MRI with dynamic contrast-enhanced (MRI-DCE) combined sensitivity and specificity were 0.81 (0.48-0.95) and 0.95 (0.92-0.97). The pooled area under the curve (AUC) of the MRI-DWI was 0.89 (95% CI: 0.86-0.91) and the pooled area under the curve (AUC) of the MRI-DCE was 0.96 (95% CI: 0.94-0.97). Discussion: The results of meta-analysis showed that multimodal MRI had good sensitivity, specificity and high sensitivity in the diagnosis of parotid gland carcinoma. However, there is high heterogeneity, which needs to be verified by a large number of clinical studies.
RESUMEN
Background: With the increasing incidence of gynecological ovarian tumors, the differential diagnosis of benign and malignant ovarian tumors is of great significance for subsequent treatment. Currently, ovarian examinations commonly use computed tomography (CT) or magnetic resonance imaging (MRI). This study sought to compare the value of CT and MRI in differentiating between benign and malignant ovarian tumors. Methods: The PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, and Weipu databases were searched for published articles using the following terms "CT" or "Computed Tomography" or "MRI" or "Magnetic Resonance imaging" and "ovarian cancer" or "ovarian tumor" or "ovarian neoplasm" or "adnexal mass" or "adnexal lesion". The articles were screened and the data were extracted based on the inclusion and exclusion criteria. The Quality Assessment of Diagnostic Accuracy Studies-2 recommended by the Cochrane Collaboration was used to assess the methodological quality of the included studies, and the network meta-analysis was performed by Stata 15.0. Results: The results showed that the overall sensitivity and specificity of CT were 0.79 [95% confidence intervals (CI): 0.70-0.87] and 0.87 (95% CI: 0.80-0.92), respectively. The overall sensitivity and specificity of MRI were 0.94 (95% CI: 0.91-0.95) and 0.91 (95% CI: 0.90-0.93), respectively. The area under the curve of the CT and MRI summary receiver operating characteristics were 0.9016 and 0.9764, respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of CT were 5.26 (95% CI: 2.78-9.93), 0.26 (95% CI: 0.13-0.50), and 22.19 (95% CI: 7.54-65.30), respectively. The positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of MRI were 8.69 (95% CI: 5.06-14.92), 0.07 (95% CI: 0.04-0.13), and 146.19 (95% CI: 68.88-310.24), respectively. Conclusions: Compared to CT, MRI has a stronger ability to differentiate between benign and malignant ovarian tumors. It's a promising non-radiological imaging technique and a more favorable choice for patients with ovarian tumors. However, in the future, large-sample, multi-center prospective studies need to be conducted to compare the performance of MRI and CT in distinguishing between benign and malignant ovarian tumors.