Journal
JAPANESE JOURNAL OF RADIOLOGY
Volume 39, Issue 7, Pages 690-702Publisher
SPRINGER
DOI: 10.1007/s11604-021-01106-8
Keywords
Deep learning; Hepatocellular cancer; Cholangiocellular carcinoma; Tumor grading; Computed tomography; X-ray
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The study developed CNN models to differentiate between intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC), as well as predict the histopathological grade of HCC. By retrospectively collecting preoperative CT scans and tumor marker information, the models showed feasibility and potential for three-class classification within primary liver cancer, with overall accuracy being 0.61 for the two-input model.
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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