Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.
Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.
复制标题
剪切波弹性成像的深度学习放射组学显着提高了评估慢性乙型肝炎肝纤维化的诊断性能：一项前瞻性多中心研究
DOI：
10.1136/gutjnl-2018-316204
复制
发表时间：
2019-04
期刊：
影响因子：
24.5
通讯作者：
Zheng R
中科院分区：
文献类型：
--
作者：
Wang K;Lu X;Zhou H;Gao Y;Zheng J;Tong M;Wu C;Liu C;Huang L;Jiang T;Meng F;Lu Y;Ai H;Xie XY;Yin LP;Liang P;Tian J;Zheng R
目的我们旨在评估新开发的深度学习弹性成像放射组学（DLRE）评估肝纤维化分期的性能。DLRE采用放射组学策略对二维剪切波弹性成像（2D-SWE）图像的不均匀性进行定量分析。设计：采用前瞻性多中心研究，以肝活检为参考标准，与2D-SWE、天冬氨酸转氨酶/血小板比值指数和基于四因素的纤维化指数进行比较，评估其在慢性B型肝炎患者中的准确性。还通过分别应用不同的采集次数和不同的训练队列来研究其准确性和鲁棒性。从12家医院前瞻性纳入654例潜在合格患者的数据，最终纳入398例患者的1990张图像。进行受试者工作特征（ROC）曲线分析，以计算肝硬化（F4）、晚期纤维化（≥ F3）和显著性纤维化（≥ F2）的ROC曲线下面积（AUC）。结果DLRE的AUC F4为0.97（95%CI 0.94~0.99），≥ F3为0.98（95%CI 0.96~1.00），≥ F2为0.85（95%CI 0.81~0.89），除≥ F2外，其他方法均优于DLRE。其诊断准确性随着从每个个体获取更多图像（特别是≥ 3个图像）而提高。没有发现显着的变化的性能，如果不同的训练队列被应用。结论与2D-SWE和生物标志物相比，DLRE在预测肝纤维化分期方面具有最佳的综合性能。该方法对HBV感染者肝纤维化分期的无创性准确诊断具有一定的实用价值。试验注册号NCT 02313649;后结果。
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.
登录
查看更多内容
DOI：
10.1002/cld.728
发表时间：
2018-04-01
期刊：
Hepatology (Baltimore, Md.)
影响因子：
--
作者：
Terrault, Norah A;Lok, Anna S F;Wong, John B
通讯作者：
Wong, John B
影响因子：
7.7
作者：
Chen, Yang;Luo, Yan;Yan, Hongmei
通讯作者：
Yan, Hongmei
影响因子：
19.7
作者：
Barr, Richard G.;Ferraioli, Giovanna;Levine, Deborah
通讯作者：
Levine, Deborah
影响因子：
13.5
作者：
Bedossa, P;Poynard, T
通讯作者：
Poynard, T
影响因子：
13.5
作者：
Sterling, Richard K.;Lissen, Eduardo;Nelson, Mark
通讯作者：
Nelson, Mark