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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Abstract

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.

Related collections

Most cited references 34

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An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of ``normal'' examples with only a small percentage of ``abnormal'' or ``interesting'' examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.

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Author and article information

Journal

Journal ID (nlm-ta): Gut

Journal ID (iso-abbrev): Gut

Journal ID (hwp): gutjnl

Journal ID (publisher-id): gut

Title: Gut

Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )

ISSN (Print): 0017-5749

ISSN (Electronic): 1468-3288

Publication date (Print): April 2019

Publication date (Electronic): 5 May 2018

Volume: 68

Issue: 4

Pages: 729-741

Affiliations

[1 ] departmentGuangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound , The Third Affiliated Hospital of Sun Yat-sen University , Guangzhou, China

[2 ] departmentCAS Key Laboratory of Molecular Imaging, Institute of Automation , Chinese Academy of Sciences , Beijing, China

[3 ] departmentDepartment of the Artificial Intelligence Technology , University of Chinese Academy of Sciences , Beijing, China

[4 ] departmentDepartment of Interventional Ultrasound , Chinese PLA General Hospital , Beijing, China

[5 ] departmentDepartment of Medical Ultrasonics , Third Hospital of Longgang , Shenzhen, China

[6 ] departmentFunctional Examination Department of Children’s Hospital , Lanzhou University Second Hospital , Lanzhou, China

[7 ] departmentUltrasound Department , The First Affiliated Hospital of Harbin Medical University , Harbin, China

[8 ] departmentUltrasound Department , Guangzhou Eighth People’s Hospital , Guangzhou, China

[9 ] departmentDepartment of Ultrasound , Shengjing Hospital of China Medical University , Shenyang, China

[10 ] departmentDepartment of Ultrasonography , The First Affiliated Hospital, Medical College of Zhejiang University , Hangzhou, China

[11 ] departmentFunction Diagnosis Center , Beijing Youan Hospital, Affiliated to Capital Medical University , Beijing, China

[12 ] departmentUltrasound Department , The Second People’s Hospital of Yunnan Province , Kunming, China

[13 ] departmentUltrasound Department , The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China

[14 ] departmentDepartment of Medical Ultrasonics , Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University , Guangzhou, China

[15 ] departmentDepartment of Ultrasound , Jiangsu Province Hospital of TCM, Affiliated Hospital of Nanjing University of TCM , Nanjing, China

Author notes

[Correspondence to ] Proffesor Ping Liang, Department of Interventional Ultrasound, Chinese PLA General Hospital, Beijing 100853, China; liangping301@ 123456hotmail.com , Proffessor Jie Tian, CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; jie.tian@ 123456ia.ac.cn and Proffessor Rongqin Zheng, Guangdong Key Laboratory of Liver Disease Research, Department of Medical Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China; zhengrq@ 123456mail.sysu.edu.cn

Article

Publisher ID: gutjnl-2018-316204

DOI: 10.1136/gutjnl-2018-316204

PMC ID: 6580779

PubMed ID: 29730602

SO-VID: cd120872-33bc-4672-85fe-f2b233ab6a26

License:

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/