Objective: The aim of this prospective study was to compare the diagnostic performances of dynamic MR imaging and CT for the differentiation of benign and malignant solitary pulmonary nodules (SPNs). Methods: Eighty-one patients with SPNs (32 malignant, 49 benign) underwent dynamic MR imaging (n = 31), dynamic CT (n = 27), or both (n = 23). The degree of peak enhancement of benign and malignant SPNs was compared on both dynamic MR imaging and CT. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performances of dynamic MR imaging and CT. Results: The malignant SPNs revealed significantly greater degrees of peak enhancement on dynamic MR imaging (mean ± SD [p%SI] 131.2 ± 46.1 versus 54.2 ± 45.3; range [p%SI] 82.6-260.0 versus −0.7-171.7; P < 0.0001) and CT (mean ± SD [DMI] 37.8 ± 15.1 versus 17.9 ± 21.8; range [DMI] 14.1-68.2 versus −5.4-107.6; P = 0.0004). Although dynamic MR imaging was somewhat superior to dynamic CT, the diagnostic performances of the 2 modalities based on ROC analysis were not statistically significant. Conclusions: Dynamic MR imaging and CT seem to be equally well suited for the differentiation between benign and malignant SPNs.
The use of NAND Flash-based SSD is on the rise in various domains such as Enterprise, Data centers, Personal computers, and Automotive. However, the address translation in the FTL has been a major performance bottleneck in the SSD systems due to frequent accesses to the L2P (Logical to Physical) mapping table. In this paper, we analyze the access patterns of logical and physical pages across popular storage workloads to find any correlations between them. The results from this paper will help system architects and researchers to design efficient address translation mechanisms for large-capacity SSDs.
Purpose: To evaluate the correlation between contrast-enhanced (CE) MRI and cerebrospinal fluid (CSF) cytology for the evaluation of leptomeningeal metastasis (LM) on MRI after targeted therapy with tyrosine kinase inhibitors. Methods: We retrospectively reviewed the data of nonsmall cell lung cancer patients registered with NCT03257124 from May 2017 to December 2018, with progressive disease despite targeted therapy. Twenty-nine patients whose MRI scans exhibited LM at the time of registration were enrolled. During the targeted therapy with osimertinib, MRI scans, and subsequent CSF examinations were performed in every 2 months. In total, 113 MRI scans and CSF cytology data after treatment were collected. For each CE MRI scan, LM positivity was evaluated on 3D T1-weighted image (T1WI) and 2D FLAIR. The correlation between MRI and CSF cytology results and the diagnostic performance of MRI with CSF cytology as a reference standard were evaluated. Results: After treatment, MRI revealed positivity for LM in 81 and negativity in 32. CSF results were positive in 69 examinations and negative in 44. The diagnostic accuracy of CE 3D T1WI and 2D FLAIR was 0.52 and 0.46, respectively. After targeted therapy, discrepancy in the CSF and MRI results tended to increase over time. The proportions of concordant MRI and CSF cytology results after targeted therapy were 66%, 58%, 62%, and 47% at the first, second, third, and fourth follow-up, respectively. Conclusion: The discrepancy of MRI in evaluation of LM and CSF cytology increases over time after targeted therapy with osimertinib. LM positivity on MRI could be a surrogate imaging marker in the pre- and immediate posttargeted-treatment with Osimertinib but not after sessions of osimertinib.
6G networks are projected to provide more individualized end-to-end services and innovative cloud-edge applications than current 5G networks. Network slicing (NS) is required when embedding 6G mobile network resources and sharing them across devices with varying needs. Recent advancements in 6G NS have been propelled by Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, which virtualize network functions. Allocating resources in 6G is a pressing issue that needs additional study. Virtual network embedding focuses primarily on node mapping and link mapping, the two most important network properties. Resource allocation in a 6G network is addressed here by using the Slice-Aware Network Embedding (SANE) technique. The slice-defined node ranking method is shown for node mapping, and the k-shortest route algorithm is presented for link mapping, both of which are contributed to by this work. The performance of the proposed strategy is measured by looking at metrics like average acceptance rates, cost-to-revenue, and bandwidth utilization. The results of this study are compared to those of other researches done in the same area to demonstrate their validity.
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