BMC Medical Imaging (Mar 2024)

Malignancy diagnosis of liver lesion in contrast enhanced ultrasound using an end-to-end method based on deep learning

  • Hongyu Zhou,
  • Jianmin Ding,
  • Yan Zhou,
  • Yandong Wang,
  • Lei Zhao,
  • Cho-Chiang Shih,
  • Jingping Xu,
  • Jianan Wang,
  • Ling Tong,
  • Zhouye Chen,
  • Qizhong Lin,
  • Xiang Jing

DOI
https://doi.org/10.1186/s12880-024-01247-y
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 10

Abstract

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Abstract Background Contrast-enhanced ultrasound (CEUS) is considered as an efficient tool for focal liver lesion characterization, given it allows real-time scanning and provides dynamic tissue perfusion information. An accurate diagnosis of liver lesions with CEUS requires a precise interpretation of CEUS images. However,it is a highly experience dependent task which requires amount of training and practice. To help improve the constrains, this study aims to develop an end-to-end method based on deep learning to make malignancy diagnosis of liver lesions using CEUS. Methods A total of 420 focal liver lesions with 136 benign cases and 284 malignant cases were included. A deep learning model based on a two-dimensional convolution neural network, a long short-term memory (LSTM), and a linear classifier (with sigmoid) was developed to analyze the CEUS loops from different contrast imaging phases. For comparison, a 3D-CNN based method and a machine-learning (ML)-based time-intensity curve (TIC) method were also implemented for performance evaluation. Results Results of the 4-fold validation demonstrate that the mean AUC is 0.91, 0.88, and 0.78 for the proposed method, the 3D-CNN based method, and the ML-based TIC method, respectively. Conclusions The proposed CNN-LSTM method is promising in making malignancy diagnosis of liver lesions in CEUS without any additional manual features selection.

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