Deep learning for staging liver fibrosis on CT: a pilot study

Abstract

Objectives

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

Methods

This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F_DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.

Results

The F_DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using F_DLCT scores were 0.74 (0.64–0.85), 0.76 (0.66–0.85) and 0.73 (0.62–0.84), respectively.

Conclusions

Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.

Key Points

• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance.

• Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging.

• Further improvement are necessary before utilisation in clinical settings.