Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study
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. A prospective m...
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| Published in | Gut Vol. 68; no. 4; pp. 729 - 741 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
BMJ Publishing Group LTD
01.04.2019
BMJ Publishing Group |
| Series | Original article |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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.
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).
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.
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.
NCT02313649; Post-results. |
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| AbstractList | 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.
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).
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.
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.
NCT02313649; Post-results. 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.OBJECTIVEWe 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.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).DESIGNA 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).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.RESULTSAUCs 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.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.CONCLUSIONDLRE 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.NCT02313649; Post-results.TRIAL REGISTRATION NUMBERNCT02313649; Post-results. ObjectiveWe 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.DesignA 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).ResultsAUCs 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.ConclusionDLRE 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 numberNCT02313649; Post-results. |
| Author | Huang, Liping Jiang, Tian’an Ai, Hong Zhou, Hui Zheng, Jian Yin, Li-ping Wang, Kun Wu, Changjun Tian, Jie Gao, Yongyan Liu, Changzhu Meng, Fankun Liang, Ping Tong, Minghui Lu, Xue Zheng, Rongqin Xie, Xiao-Yan Lu, Yongping |
| AuthorAffiliation | 11 Function Diagnosis Center , Beijing Youan Hospital, Affiliated to Capital Medical University , Beijing , China 13 Ultrasound Department , The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an , China 10 Department of Ultrasonography , The First Affiliated Hospital, Medical College of Zhejiang University , Hangzhou , China 8 Ultrasound Department , Guangzhou Eighth People’s Hospital , Guangzhou , China 4 Department of Interventional Ultrasound , Chinese PLA General Hospital , Beijing , China 12 Ultrasound Department , The Second People’s Hospital of Yunnan Province , Kunming , China 3 Department of the Artificial Intelligence Technology , University of Chinese Academy of Sciences , Beijing , China 14 Department of Medical Ultrasonics , Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou , China 9 Department of Ultrasound , Shengjing Hospital of China Medical University , Shenyang , China 15 Department of Ultra |
| AuthorAffiliation_xml | – name: 6 Functional Examination Department of Children’s Hospital , Lanzhou University Second Hospital , Lanzhou , China – name: 4 Department of Interventional Ultrasound , Chinese PLA General Hospital , Beijing , China – name: 7 Ultrasound Department , The First Affiliated Hospital of Harbin Medical University , Harbin , China – name: 9 Department of Ultrasound , Shengjing Hospital of China Medical University , Shenyang , China – name: 11 Function Diagnosis Center , Beijing Youan Hospital, Affiliated to Capital Medical University , Beijing , China – name: 1 Guangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound , The Third Affiliated Hospital of Sun Yat-sen University , Guangzhou , China – name: 15 Department of Ultrasound , Jiangsu Province Hospital of TCM, Affiliated Hospital of Nanjing University of TCM , Nanjing , China – name: 13 Ultrasound Department , The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an , China – name: 14 Department of Medical Ultrasonics , Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou , China – name: 12 Ultrasound Department , The Second People’s Hospital of Yunnan Province , Kunming , China – name: 5 Department of Medical Ultrasonics , Third Hospital of Longgang , Shenzhen , China – name: 8 Ultrasound Department , Guangzhou Eighth People’s Hospital , Guangzhou , China – name: 10 Department of Ultrasonography , The First Affiliated Hospital, Medical College of Zhejiang University , Hangzhou , China – name: 2 CAS Key Laboratory of Molecular Imaging, Institute of Automation , Chinese Academy of Sciences , Beijing , China – name: 3 Department of the Artificial Intelligence Technology , University of Chinese Academy of Sciences , Beijing , China |
| Author_xml | – sequence: 1 givenname: Kun surname: Wang fullname: Wang, Kun – sequence: 2 givenname: Xue surname: Lu fullname: Lu, Xue – sequence: 3 givenname: Hui surname: Zhou fullname: Zhou, Hui – sequence: 4 givenname: Yongyan surname: Gao fullname: Gao, Yongyan – sequence: 5 givenname: Jian surname: Zheng fullname: Zheng, Jian – sequence: 6 givenname: Minghui surname: Tong fullname: Tong, Minghui – sequence: 7 givenname: Changjun surname: Wu fullname: Wu, Changjun – sequence: 8 givenname: Changzhu surname: Liu fullname: Liu, Changzhu – sequence: 9 givenname: Liping surname: Huang fullname: Huang, Liping – sequence: 10 givenname: Tian’an surname: Jiang fullname: Jiang, Tian’an – sequence: 11 givenname: Fankun surname: Meng fullname: Meng, Fankun – sequence: 12 givenname: Yongping surname: Lu fullname: Lu, Yongping – sequence: 13 givenname: Hong surname: Ai fullname: Ai, Hong – sequence: 14 givenname: Xiao-Yan surname: Xie fullname: Xie, Xiao-Yan – sequence: 15 givenname: Li-ping surname: Yin fullname: Yin, Li-ping – sequence: 16 givenname: Ping surname: Liang fullname: Liang, Ping – sequence: 17 givenname: Jie surname: Tian fullname: Tian, Jie – sequence: 18 givenname: Rongqin surname: Zheng fullname: Zheng, Rongqin |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29730602$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1055/s-0043-103952 10.1038/nature14539 10.1148/radiol.2016160131 10.1148/radiol.2015151169 10.1613/jair.953 10.1148/radiol.2015150619 10.1002/hep.510240201 10.1002/hep.25936 10.1111/jgh.12220 10.1016/j.jhep.2014.04.044 10.1200/JCO.2015.65.9128 10.1109/ACCESS.2017.2689058 10.1016/j.ultrasmedbio.2017.05.002 10.3748/wjg.v20.i28.9578 10.1002/hep.29179 10.1007/s00330-014-3292-9 10.1016/j.jhep.2015.11.012 10.1158/1078-0432.CCR-15-2997 10.1109/TMI.2016.2535302 10.1053/jhep.2003.50346 10.1056/NEJM200102153440706 10.1109/TMI.2016.2528162 10.1148/radiol.14140839 10.1016/j.jhep.2010.05.035 10.1002/hep.21178 10.1016/j.compbiomed.2017.07.012 10.1148/radiol.14131164 10.1002/hep.29800 10.1148/radiol.14140828 10.1148/radiol.13130128 10.1016/j.jhep.2017.03.021 10.1016/j.cgh.2014.12.014 10.3390/s17010149 |
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| References | Lu (2020050721341777000_68.4.729.8) 2015; 275 Meng (2020050721341777000_68.4.729.18) 2017; 5 Zeng (2020050721341777000_68.4.729.24) 2014; 24 Nitesh (2020050721341777000_68.4.729.28) 2002; 16 2020050721341777000_68.4.729.4 2020050721341777000_68.4.729.3 2020050721341777000_68.4.729.2 Liu (2020050721341777000_68.4.729.21) 2017; 17 Cassinotto (2020050721341777000_68.4.729.9) 2014; 61 2020050721341777000_68.4.729.5 Tajbakhsh (2020050721341777000_68.4.729.23) 2016; 35 Gatos (2020050721341777000_68.4.729.20) 2017; 43 Cui (2020050721341777000_68.4.729.1) 2013; 28 Suppl 1 2020050721341777000_68.4.729.27 Zhuang (2020050721341777000_68.4.729.12) 2017; 283 Barr (2020050721341777000_68.4.729.7) 2015; 276 2020050721341777000_68.4.729.25 2020050721341777000_68.4.729.26 Zheng (2020050721341777000_68.4.729.13) 2015; 275 Afdhal (2020050721341777000_68.4.729.30) 2015; 13 Samir (2020050721341777000_68.4.729.14) 2015; 274 2020050721341777000_68.4.729.22 Herrmann (2020050721341777000_68.4.729.31) 2018; 67 Huang (2020050721341777000_68.4.729.33) 2014; 20 2020050721341777000_68.4.729.29 Dietrich (2020050721341777000_68.4.729.6) 2017; 38 Chen (2020050721341777000_68.4.729.19) 2017; 89 2020050721341777000_68.4.729.16 2020050721341777000_68.4.729.17 2020050721341777000_68.4.729.15 2020050721341777000_68.4.729.10 2020050721341777000_68.4.729.32 2020050721341777000_68.4.729.11 |
| References_xml | – volume: 38 start-page: e16 year: 2017 ident: 2020050721341777000_68.4.729.6 article-title: EFSUMB Guidelines and Recommendations on the Clinical Use of Liver Ultrasound Elastography, Update 2017 (Long Version) publication-title: Ultraschall Med doi: 10.1055/s-0043-103952 – ident: 2020050721341777000_68.4.729.22 doi: 10.1038/nature14539 – volume: 283 start-page: 873 year: 2017 ident: 2020050721341777000_68.4.729.12 article-title: Two-dimensional Shear-Wave Elastography Performance in the Noninvasive Evaluation of Liver Fibrosis in Patients with Chronic Hepatitis B: Comparison with Serum Fibrosis Indexes publication-title: Radiology doi: 10.1148/radiol.2016160131 – ident: 2020050721341777000_68.4.729.15 doi: 10.1148/radiol.2015151169 – volume: 16 start-page: 321 year: 2002 ident: 2020050721341777000_68.4.729.28 article-title: SMOTE: synthetic minority over-sampling technique publication-title: J Artif Intell Res doi: 10.1613/jair.953 – volume: 276 start-page: 845 year: 2015 ident: 2020050721341777000_68.4.729.7 article-title: Elastography Assessment of Liver Fibrosis: Society of Radiologists in Ultrasound Consensus Conference Statement publication-title: Radiology doi: 10.1148/radiol.2015150619 – ident: 2020050721341777000_68.4.729.25 doi: 10.1002/hep.510240201 – ident: 2020050721341777000_68.4.729.10 doi: 10.1002/hep.25936 – volume: 28 Suppl 1 start-page: 7 issue: Suppl 1 year: 2013 ident: 2020050721341777000_68.4.729.1 article-title: Update on epidemiology of hepatitis B and C in China publication-title: J Gastroenterol Hepatol doi: 10.1111/jgh.12220 – volume: 61 start-page: 550 year: 2014 ident: 2020050721341777000_68.4.729.9 article-title: Non-invasive assessment of liver fibrosis with impulse elastography: comparison of Supersonic Shear Imaging with ARFI and FibroScan® publication-title: J Hepatol doi: 10.1016/j.jhep.2014.04.044 – ident: 2020050721341777000_68.4.729.16 doi: 10.1200/JCO.2015.65.9128 – volume: 5 start-page: 1 year: 2017 ident: 2020050721341777000_68.4.729.18 article-title: Liver fibrosis classification based on transfer learning and FCNet for ultrasound images publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2689058 – volume: 43 start-page: 1797 year: 2017 ident: 2020050721341777000_68.4.729.20 article-title: A Machine-Learning Algorithm Toward Color Analysis for Chronic Liver Disease Classification, Employing Ultrasound Shear Wave Elastography publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2017.05.002 – volume: 20 start-page: 9578 year: 2014 ident: 2020050721341777000_68.4.729.33 article-title: Study of detection times for liver stiffness evaluation by shear wave elastography publication-title: World J Gastroenterol doi: 10.3748/wjg.v20.i28.9578 – volume: 67 start-page: 260 year: 2018 ident: 2020050721341777000_68.4.729.31 article-title: Assessment of biopsy-proven liver fibrosis by two-dimensional shear wave elastography: An individual patient data-based meta-analysis publication-title: Hepatology doi: 10.1002/hep.29179 – volume: 24 start-page: 2572 year: 2014 ident: 2020050721341777000_68.4.729.24 article-title: Diagnostic accuracy of two-dimensional shear wave elastography for the non-invasive staging of hepatic fibrosis in chronic hepatitis B: a cohort study with internal validation publication-title: Eur Radiol doi: 10.1007/s00330-014-3292-9 – ident: 2020050721341777000_68.4.729.4 doi: 10.1016/j.jhep.2015.11.012 – ident: 2020050721341777000_68.4.729.17 doi: 10.1158/1078-0432.CCR-15-2997 – volume: 35 start-page: 1299 year: 2016 ident: 2020050721341777000_68.4.729.23 article-title: Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? publication-title: IEEE Trans Med Imaging doi: 10.1109/TMI.2016.2535302 – ident: 2020050721341777000_68.4.729.26 doi: 10.1053/jhep.2003.50346 – ident: 2020050721341777000_68.4.729.3 doi: 10.1056/NEJM200102153440706 – ident: 2020050721341777000_68.4.729.29 doi: 10.1109/TMI.2016.2528162 – volume: 274 start-page: 888 year: 2015 ident: 2020050721341777000_68.4.729.14 article-title: Shear-wave elastography for the estimation of liver fibrosis in chronic liver disease: determining accuracy and ideal site for measurement publication-title: Radiology doi: 10.1148/radiol.14140839 – ident: 2020050721341777000_68.4.729.32 doi: 10.1016/j.jhep.2010.05.035 – ident: 2020050721341777000_68.4.729.27 doi: 10.1002/hep.21178 – volume: 89 start-page: 18 year: 2017 ident: 2020050721341777000_68.4.729.19 article-title: Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2017.07.012 – volume: 275 start-page: 880 year: 2015 ident: 2020050721341777000_68.4.729.8 article-title: Hepatocellular carcinoma: stiffness value and ratio to discriminate malignant from benign focal liver lesions publication-title: Radiology doi: 10.1148/radiol.14131164 – ident: 2020050721341777000_68.4.729.2 doi: 10.1002/hep.29800 – volume: 275 start-page: 290 year: 2015 ident: 2020050721341777000_68.4.729.13 article-title: Two-dimensional shear-wave elastography and conventional US: the optimal evaluation of liver fibrosis and cirrhosis publication-title: Radiology doi: 10.1148/radiol.14140828 – ident: 2020050721341777000_68.4.729.11 doi: 10.1148/radiol.13130128 – ident: 2020050721341777000_68.4.729.5 doi: 10.1016/j.jhep.2017.03.021 – volume: 13 start-page: 772 year: 2015 ident: 2020050721341777000_68.4.729.30 article-title: Accuracy of fibroscan, compared with histology, in analysis of liver fibrosis in patients with hepatitis B or C: a United States multicenter study publication-title: Clin Gastroenterol Hepatol doi: 10.1016/j.cgh.2014.12.014 – volume: 17 start-page: 149 year: 2017 ident: 2020050721341777000_68.4.729.21 article-title: Learning to Diagnose Cirrhosis with Liver Capsule Guided Ultrasound Image Classification publication-title: Sensors doi: 10.3390/s17010149 |
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| Snippet | We aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis stages. DLRE adopts the... ObjectiveWe aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis stages. DLRE... |
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| SubjectTerms | Accuracy Adult Aspartate transaminase Automation Bile Biomarkers Biopsy China Cirrhosis Classification Deep Learning Diagnosis, Differential Elasticity Imaging Techniques - methods Female Fibrosis Hepatitis Hepatitis B Hepatitis B, Chronic - complications Hepatology Histology Humans Interferon Laboratories Liver Liver cirrhosis Liver Cirrhosis - diagnostic imaging Liver Cirrhosis - pathology Liver diseases Male Methods Neural networks Patients Prospective Studies Quantitative analysis Radiomics Studies Surveillance Transaminase Ultrasonic imaging |
| Title | Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study |
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