Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study

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.

Keywords: cirrhosis; hepatitis B; ultrasonography.

Figure 1

Illustration of the two-dimensional shear wave elastography (2D-SWE) measurement and the deep learning Radiomics of elastography (DLRE) flow chart. (A) The top shows the 2D-SWE region of interest (ROI) (pseudocolour area), Q-Box (white circle area within 2D-SWE ROI) and DLRE ROI (red square area). The obtained 2D-SWE values are displayed on the right yellow box. The bottom is the corresponding B-mode ultrasound image. (B) An input layer (DLRE ROI) is analysed by using four convolution-pooling procedures (C1-P1 to C4-P4), and then last pooled maps are fully connected with 32 neural nodes to calculate its probability for classification. The neural nodes and other parameters of the convolutional neural network (CNN) model were automatically optimised by using all 2D-SWE images in the training cohort.

Figure 2

The results of the multicentre patient enrolments. In total, 398 out of 654 patients from 12 Chinese hospitals were enrolled in this study. 2D-SWE, two-dimensional shear wave elastography.

Figure 3

Comparison of ROC curves between DLRE, 2D-SWE and biomarkers for the assessment of liver fibrosis stages in training and validation cohorts, respectively. (A, D) F0-F3 versus F4 (F4) in training and validation cohorts. (B, E) F0-F2 versus F3-F4 (≥F3) in training and validation cohorts. (C, F) F0-F1 versus F2-F4 (≥F2) in training and validation cohorts. 2D-SWE, two-dimensional shear wave elastography; APRI, aspartate transaminase-to-platelet ratio index; DLRE, deep learning Radiomics of elastography; FIB-4, fibrosis index based on four factors.

Figure 4

Comparison of receiver operating characteristic (ROC) curves between deep learning Radiomics of elastography (DLRE) and two-dimensional shear wave elastography (2D-SWE) using different number of image acquisitions/measurements (1, 3 and 5) of each patient for the assessment of liver fibrosis stages. (A, D) F0-F3 versus F4 (F4) in training and validation cohorts. (B, E) F0-F2 versus F3-F4 (≥F3) in training and validation cohorts. (C, F) F0-F1 versus F2-F4 (≥F2) in training and validation cohorts.

Figure 5

Comparison of receiver operating characteristic (ROC) curves between different combinations of hospitals for training deep learning Radiomics of elastography (DLRE) in the classification of liver fibrosis stages. (A, D) F0-F3 versus F4 (F4) in training (combination of hospitals B, D, G, E, H and J) and validation cohorts. (B, E) F0-F2 versus F3-F4 (≥F3) in training (combination of hospitals A, C and K) and validation cohorts. (C, F) F0-F1 versus F2-F4 (≥F2) in training (combination of hospitals A, G and K) and validation cohorts. Note: three ROC curves completely overlap each other in (A) and (C), as they all reach the optimal profile (area under the receiver operating characteristic curve (AUC)=1).