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USC-ENet: a high-efficiency model for the diagnosis of liver tumors combining B-mode ultrasound and clinical data

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Abstract

Purpose

Ultrasound image acquisition has the advantages of being low cost, rapid, and non-invasive, and it does not produce radiation. Currently, ultrasound is widely used in the diagnosis of liver tumors. However, owing to the complex presentation and diverse features of benign and malignant liver tumors, accurate diagnosis of liver tumors using ultrasound is difficult even for experienced radiologists. In recent years, artificial intelligence-assisted diagnosis has proven to provide effective support to radiologists. However, there is room for further improvement in the existing ultrasound artificial intelligence diagnostic model of liver tumor. First, the image diagnostic model may not fully consider relevant clinical data in the decision-making process. Second, owing to the difficulty in collecting biopsy pathology and physician-labeled ultrasound data of liver tumors, training datasets are usually small, and commonly used large neural networks tend to overfit on small datasets, which seriously affects the generalization of the model.

Methods

In this study, we propose a deep learning–assisted diagnosis model called USC-ENet, which integrates B-mode ultrasound features of liver tumors and clinical data of patients, and we design a small neural network specifically for small-scale medical images combined with an attention mechanism.

Results and conclusion

Real data from 542 patients with liver tumors (N = 2168 images) are used during model training and validation. Experiments show that USC-ENet can achieve a good classification effect (area under the curve = 0.956, sensitivity = 0.915, and specificity = 0.880) after small-scale data training, and it has certain interpretability, showing good potential for clinical adoption. In conclusion, our model provides not only a reliable second opinion for radiologists but also a reference for junior radiologists who lack clinical experience.

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Data & Code availability

The datasets and code generated during the current study are available from the corresponding author on reasonable request

Notes

  1. The datasets and code generated during the current study are available from the corresponding author on reasonable request.

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Funding

This study was funded by Yunnan Provincial Science and Technology Department(202201AY070001)

Author information

Authors and Affiliations

  1. School of Statistics and Mathematics, Yunnan University of Finance and Economics, Longquan Road, Kunming, 650221, Yunnan, China

    Tingting Zhao, Zhiyong Zeng, Tong Li, Xing Yu & Tao Feng

  2. Department of Medical Ultrasound, The Second Affiliated Hospital of Kunming Medical University, Dianmian Road, Kunming, 650101, Yunnan, China

    Wenjing Tao & Rui Bu

Authors
  1. Tingting Zhao
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  2. Zhiyong Zeng
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  3. Tong Li
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  4. Wenjing Tao
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  5. Xing Yu
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  6. Tao Feng
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  7. Rui Bu
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Contributions

Tingting Zhao developed the methods and performed the statistical analysis; Tao Feng and Rui Bu designed and supervised the study, Zhiyong Zeng revised the manuscript, and Wenjing Tao, Tong Li, and Xing Yu collected the datasets. All authors contributed to the writing and the interpretation of the results

Corresponding authors

Correspondence to Tao Feng or Rui Bu.

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The authors declare that they have no conflict of interest

Ethical approval

This research was approved by the authors’ Institutional Review Board (Medical Ethics Committee of the Second Affiliated Hospital of Kunming Medical University)

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Zhao, T., Zeng, Z., Li, T. et al. USC-ENet: a high-efficiency model for the diagnosis of liver tumors combining B-mode ultrasound and clinical data. Health Inf Sci Syst 11, 15 (2023). https://doi.org/10.1007/s13755-023-00217-y

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