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

Authors
  • Nakai, Hirotsugu1
  • Fujimoto, Koji1, 1
  • Yamashita, Rikiya2
  • Sato, Toshiyuki1
  • Someya, Yuko1
  • Taura, Kojiro1
  • Isoda, Hiroyoshi1, 3
  • Nakamoto, Yuji1
  • 1 Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan , Kyoto (Japan)
  • 2 Stanford University School of Medicine, 1265 Welch Road, Stanford, CA, 94305, USA , Stanford (United States)
  • 3 Kyoto University Hospital, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan , Kyoto (Japan)
Type
Published Article
Journal
Japanese Journal of Radiology
Publisher
Springer Singapore
Publication Date
Mar 10, 2021
Volume
39
Issue
7
Pages
690–702
Identifiers
DOI: 10.1007/s11604-021-01106-8
Source
Springer Nature
Keywords
Disciplines
  • Original Article
License
Yellow

Abstract

PurposeTo develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.Materials and methodsPreoperative 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.ResultsThe 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).ConclusionOur CNN model with tumor marker information showed feasibility and potential for three-class classification within primary liver cancer.

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