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Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma

  • Author Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Xiachuan Qin
    Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, China, 230022

    Department of Ultrasound, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College (University), Nan Chong, Sichuan, China
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  • Author Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Jianhui Zhu
    Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Affiliations
    Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
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  • Author Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Zhengzheng Tu
    Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
    Affiliations
    Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
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  • Qianqing Ma
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, China, 230022
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  • Jin Tang
    Affiliations
    Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei, Anhui, China
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  • Chaoxue Zhang
    Correspondence
    Address correspondence to: C.Z., No. 218 Jixi Road, Shushan District, Hefei 230022, Anhui Province, China
    Affiliations
    Department of Ultrasound, The First Affiliated Hospital of Anhui Medical University, Hefei, AH, China, 230022
    Search for articles by this author
  • Author Footnotes
    # Xiachuan Qin, Jianhui Zhu and Zhengzheng Tu contributed equally to this paper.
Published:December 23, 2022DOI:https://doi.org/10.1016/j.acra.2022.12.005

      Rationale and Objectives

      Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision-making regarding the treatment strategy and evaluation of patient prognosis. We developed a deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict MVI of HCC.

      Materials and Methods

      We retrospectively analyzed the data for single primary HCCs that were evaluated with CEUS 1 week before surgical resection from December 2014 to February 2022. The study population was divided into training (n = 198) and test (n = 54) cohorts. In this study, three DL models (Resnet50, Resnet50+BAM, Resnet50+SE) were trained using the training cohort and tested in the test cohort. Tumor characteristics were also evaluated by radiologists, and multivariate regression analysis was performed to determine independent indicators for the development of predictive nomogram models. The performance of the three DL models was compared to that of the MVI prediction model based on radiologist evaluations.

      Results

      The best-performing model, ResNet50+SE model achieved the ROC of 0.856, accuracy of 77.2, specificity of 93.9%, and sensitivity of 52.4% in the test group. The MVI prediction model based on a combination of three independent predictors showed a C-index of 0.729, accuracy of 69.4, specificity of 73.8%, and sensitivity of 62%.

      Conclusion

      The DL algorithm can accurately predict MVI of HCC on the basis of CEUS images, to help identify high-risk patients for the assist treatment.

      Key Words

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