Objective We aimed to evaluate the performance of the newly developed deep learning radiomics of elastography (Dlre) for assessing liver fibrosis stages. Dlre adopts the radiomic strategy for quantitative analysis of the heterogeneity in two-dimensional shear wave elastography (2D-SWe) images. Design a prospective multicentre study was conducted to assess its accuracy in patients with chronic hepatitis B, in comparison with 2D-SWe, aspartate transaminaseto-platelet ratio index and fibrosis index based on four factors, by using liver biopsy as the reference standard. its accuracy and robustness were also investigated by applying different number of acquisitions and different training cohorts, respectively. Data of 654 potentially eligible patients were prospectively enrolled from 12 hospitals, and finally 398 patients with 1990 images were included. analysis of receiver operating characteristic (rOc) curves was performed to calculate the optimal area under the rOc curve (aUc) for cirrhosis (F4), advanced fibrosis (≥F3) and significance fibrosis (≥F2). results aUcs of Dlre were 0.97 for F4 (95% ci 0.94 to 0.99), 0.98 for ≥F3 (95% ci 0.96 to 1.00) and 0.85 (95% ci 0.81 to 0.89) for ≥F2, which were significantly better than other methods except 2D-SWe in ≥F2. its diagnostic accuracy improved as more images (especially ≥3 images) were acquired from each individual. no significant variation of the performance was found if different training cohorts were applied.

Purpose To investigate the diagnostic performance of two-dimensional (2D) shear-wave elastography (SWE) in chronic hepatitis B. Materials and Methods This prospective multicenter study from January 2015 to January 2016 was conducted at 12 hospitals and included 654 participants with chronic hepatitis B who had undergone liver biopsy and 2D SWE examination. Participants were divided into chronic infection and chronic hepatitis groups. The diagnostic performance of 2D SWE was compared with the aspartate amino transferase-to-platelet ratio index (APRI), the Fibrosis-4 index (FIB-4), and transient elastography (TE) by using a DeLong test and was also compared between two subgroups. Dual cutoff values for cirrhosis were determined with multilevel likelihood ratio analysis. Results Overall, 402 participants with chronic hepatitis B were enrolled (154 with chronic infection and 248 with chronic hepatitis). The areas under the receiver operating characteristic curve of 2D SWE (0.87; 95% confidence interval [CI]: 0.83, 0.90) were higher than those of TE (0.80; 95% CI: 0.68, 0.88), APRI (0.70; 95% CI: 0.65, 0.74), and FIB-4 (0.73; 95% CI: 0.69, 0.78) in cirrhosis. The high area under the receiver operating characteristic curve (0.92; 95% CI: 0.87, 0.96) was achieved in the chronic infection group and was significantly higher than that of the chronic hepatitis group (0.84; 95% CI: 0.78, 0.88; P = .017). Dual cutoff values with the likelihood ratios below 0.1 and above 10 (8.4 kPa and 11.0 kPa to rule out and rule in a diagnosis of cirrhosis, respectively) were effectively determined in chronic infection; a total of 81.2% (125 of 154) participants with cirrhosis were definitively diagnosed. Conclusion The performance of two-dimensional (2D) shear-wave elastography (SWE) was higher than that of other noninvasive methods. 2D SWE was most effective in ruling in and ruling out cirrhosis in participants with chronic infection, which may prompt antiviral treatment. © RSNA, 2018 Online supplemental material is available for this article.

Purpose To explore the usefulness of liver stiffness measurements (LSMs) by sound touch elastography (STE) and sound touch quantification (STQ) in chronic hepatitis B (CHB) patients for staging fibrosis. Methods This prospective multicenter study recruited normal volunteers and CHB patients between May 2018 and October 2019. The volunteers underwent LSM by STE and supersonic shear imaging (SSI) or by STQ and acoustic radiation force impulse imaging (ARFI). CHB patients underwent liver biopsy and LSM by both STE/STQ. The areas under the receiver operating characteristic curves (AUCs) for staging fibrosis were calculated. Results Overall, 97 volunteers and 524 CHB patients were finally eligible for the study. The successful STE and STQ measurement rates were both 100 % in volunteers and 99.4 % in CHB patients. The intraclass correlation coefficients (ICCs) for the intra-observer stability of STE and STQ (0.94; 0.90) were similar to those of SSI and ARFI (0.95; 0.87), respectively. STE and STQ showed better accuracy than the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 index (FIB-4) (AUC: 0.87 vs 0.86 vs 0.73 vs 0.77) in staging cirrhosis. However, both STE and STQ were not superior to APRI and FIB-4 in staging significant fibrosis (AUC: 0.76 vs 0.73 vs 0.70 vs 0.71, all P-values > 0.05). Conclusion STE and STQ are convenient techniques with a reliable LSM value. They have a similar diagnostic performance and are superior to serum biomarkers in staging cirrhosis in CHB patients.

In recent years, machine-to-machine (M2M) communications have attracted great attentions from both academia and industry. In M2M communication systems, machine type communication devices (MTCDs) are capable of communicating with each other intelligently under highly reduced human interventions. Although diverse types of services are expected to be supported for MTCDs, various quality of service (QoS) requirements and network states pose difficulties and challenges to the resource allocation and clustering schemes of M2M communication systems. In this paper, we address the joint resource allocation and clustering problem in M2M communication systems. To achieve the efficient resource management of the MTCDs, we propose a joint resource management architecture, and design a joint resource allocation and clustering algorithm. More specifically, by defining system energy efficiency as the sum of the energy efficiency of the MTCDs, the joint resource allocation and clustering problem is formulated as an energy efficiency maximization problem. As the original optimization problem is a nonlinear fractional programming problem, which cannot be solved conveniently, we transform the optimization problem into power allocation subproblem and clustering subproblem. Applying iterative method-based energy efficiency maximization algorithm, we first obtain the optimal power allocation strategy based on which, we then propose a modified K-means algorithm to obtain the clustering strategy. Numerical results demonstrate the effectiveness of the proposed algorithm. INDEX TERMS Machine-to-machine (M2M) communications, resource allocation, clustering, energy efficiency.