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Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study

Abstract

Purpose

To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.

Materials and methods

Preoperative computed tomography and tumor marker information of 617 primary liver cancer patients were retrospectively collected to develop CNN models categorizing tumors into three categories: moderately differentiated HCC (mHCC), poorly differentiated HCC (pHCC), and ICC, where the histopathological diagnoses were considered as ground truths. The models processed manually cropped tumor with and without tumor marker information (two-input and one-input models, respectively). Overall accuracy was assessed using a held-out dataset (10%). Area under the curve, sensitivity, and specificity for differentiating ICC from HCCs (mHCC + pHCC), and pHCC from mHCC were also evaluated. We assessed two radiologists’ performance without tumor marker information as references (overall accuracy, sensitivity, and specificity). The two-input model was compared with the one-input model and radiologists using permutation tests.

Results

The overall accuracy was 0.61, 0.60, 0.55, 0.53 for the two-input model, one-input model, radiologist 1, and radiologist 2, respectively. For differentiating pHCC from mHCC, the two-input model showed significantly higher specificity than radiologist 1 (0.68 [95% confidence interval: 0.50–0.83] vs 0.45 [95% confidence interval: 0.27–0.63]; p = 0.04).

Conclusion

Our CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

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Abbreviations

AFP:

Alpha-fetoprotein

CA 19-9:

Carbohydrate antigen 19-9

CEA:

Carcinoembryonic antigen

CNN:

Convolutional neural network

HCC:

Hepatocellular carcinoma

HU:

Hounsfield units

ICC:

Intrahepatic cholangiocarcinoma

LI-RADS:

Liver imaging reporting and data system

mHCC:

Moderately differentiated hepatocellular carcinoma

pHCC:

Poorly differentiated hepatocellular carcinoma

PIVKA-II:

Prothrombin induced by vitamin K absence-II

ROC:

Receiver-operating characteristic

ROI:

Region of interest

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Acknowledgments

We thank Andrea Baird, MD, from Edanz Group (https://en-author-services.edanzgroup.com/ac) for editing a draft of this manuscript.

Funding

We have no funding to declare.

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Corresponding author

Correspondence to Hirotsugu Nakai.

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Ethical statement

This study was approved by our institutional review board and was conducted according to the principles of the Declaration of Helsinki.

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This article has not been published or presented elsewhere in part or in entirety, and is not under consideration by another journal. Each author has participated sufficiently in the study to take public responsibility for it. The authors have all approved the manuscript.

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Nakai, H., Fujimoto, K., Yamashita, R. et al. Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study. Jpn J Radiol 39, 690–702 (2021). https://doi.org/10.1007/s11604-021-01106-8

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  • DOI: https://doi.org/10.1007/s11604-021-01106-8

Keywords

  • Deep learning
  • Hepatocellular cancer
  • Cholangiocellular carcinoma
  • Tumor grading
  • Computed tomography
  • X-ray