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.

We report the large-scale synthesis of silicon nanowires (SiNWs) using a simple but effective approach. High purity SiNWs of uniform diameters around 15 nm were obtained by sublimating a hot-pressed silicon powder target at 1200 °C in a flowing carrier gas environment. The SiNWs emit stable blue light which seems unrelated to quantum confinement, but related to an amorphous overcoating layer of silicon oxide. Our approach can be used, in principle, as a general method for synthesis of other one-dimensional semiconducting, or conducting nanowires.

No abstract