RT Journal Article
SR Electronic
T1 Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study
JF Gut
JO Gut
FD BMJ Publishing Group Ltd and British Society of Gastroenterology
SP gutjnl-2018-316204
DO 10.1136/gutjnl-2018-316204
A1 Kun Wang
A1 Xue Lu
A1 Hui Zhou
A1 Yongyan Gao
A1 Jian Zheng
A1 Minghui Tong
A1 Changjun Wu
A1 Changzhu Liu
A1 Liping Huang
A1 Tian’an Jiang
A1 Fankun Meng
A1 Yongping Lu
A1 Hong Ai
A1 Xiao-Yan Xie
A1 Li-ping Yin
A1 Ping Liang
A1 Jie Tian
A1 Rongqin Zheng
YR 2018
UL http://gut.bmj.com/content/early/2018/05/04/gutjnl-2018-316204.abstract
AB 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 transaminase-to-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.Conclusion DLRE shows the best overall performance in predicting liver fibrosis stages compared with 2D-SWE and biomarkers. It is valuable and practical for the non-invasive accurate diagnosis of liver fibrosis stages in HBV-infected patients.Trial registration number NCT02313649; Post-results.