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Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma

  • Hepatobiliary
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objectives

The current study developed an ultrasound-based deep learning model to make preoperative differentiation among hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and combined hepatocellular–cholangiocarcinoma (cHCC-ICC).

Methods

The B-mode ultrasound images of 465 patients with primary liver cancer were enrolled in model construction, comprising 264 HCCs, 105 ICCs, and 96 cHCC-ICCs, of which 50 cases were randomly selected to form an independent test cohort, and the rest of study population was assigned to a training and validation cohorts at the ratio of 4:1. Four deep learning models (Resnet18, MobileNet, DenseNet121, and Inception V3) were constructed, and the fivefold cross-validation was adopted to train and validate the performance of these models. The following indexes were calculated to determine the differential diagnosis performance of the models, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), F-1 score, and area under the receiver operating characteristic curve (AUC) based on images in the independent test cohort.

Results

Based on the fivefold cross-validation, the Resnet18 outperformed other models in terms of accuracy and robustness, with the overall training and validation accuracy as 99.73% (± 0.07%) and 99.35% (± 0.53%), respectively. Furthers validation based on the independent test cohort suggested that Resnet 18 yielded the best diagnostic performance in identifying HCC, ICC, and cHCC-ICC, with the sensitivity, specificity, accuracy, PPV, NPV, F1-score, and AUC of 84.59%, 92.65%, 86.00%, 85.82%, 92.99%, 92.37%, 85.07%, and 0.9237 (95% CI 0.8633, 0.9840).

Conclusion

Ultrasound-based deep learning algorithm appeared a promising diagnostic method for identifying cHCC-ICC, HCC, and ICC, which might play a role in clinical decision making and evaluation of prognosis.

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Acknowledgement

Not applicable.

Funding

The research was supported by Clinical Study of Shanghai Municipal Health Commission (No. 20194Y0473).

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Authors and Affiliations

  1. The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong, China

    Jianan Chen

  2. Department of Radiology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China

    Yanling Chen

  3. Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, China

    Qiannan Zhao, Yuli Zhu & Yanling Chen

  4. Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China

    Weibin Zhang

  5. School of Medical Science, Hexi University, Zhangye, China

    Jingwen Bao

  6. Department of Ultrasound, The Affiliated Hospital of Binzhou Medical University, Binzhou, China

    Kun Wang

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Correspondence to Yuli Zhu or Yanling Chen.

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Chen, J., Zhang, W., Bao, J. et al. Implications of ultrasound-based deep learning model for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Abdom Radiol 49, 93–102 (2024). https://doi.org/10.1007/s00261-023-04089-4

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