ArticleJapanese journal of radiology2021
Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.
Article in Japanese journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.
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Who cites it
26 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice.Japanese journal of radiology · 2026Pooled it
- Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.Journal of imaging informatics in medicine · 2024Pooled it
- Deep learning and machine learning in image-based hepatocellular carcinoma detection: a systematic review and meta-analysis.Abdominal radiology (New York) · 2026Review
- Radiomics for Detection and Differentiation of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- Review
- Artificial intelligence for detection and characterization of focal hepatic lesions: a review.Abdominal radiology (New York) · 2025Review
- An Artificial Intelligence Pipeline for Hepatocellular Carcinoma: From Data to Treatment Recommendations.International journal of general medicine · 2025Review
- Advancements in Artificial Intelligence-Enhanced Imaging Diagnostics for the Management of Liver Disease-Applications and Challenges in Personalized Care.Bioengineering (Basel, Switzerland) · 2024Review
- Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.Tomography (Ann Arbor, Mich.) · 2024Review
- Application of AI on cholangiocarcinoma.Frontiers in oncology · 2024Review
- Fairness of artificial intelligence in healthcare: review and recommendations.Japanese journal of radiology · 2024Review
- Artificial intelligence techniques in liver cancer.Frontiers in oncology · 2024Review
- Deep learning-assisted LI-RADS grading and distinguishing hepatocellular carcinoma (HCC) from non-HCC based on multiphase CT: a two-center study.European radiology · 2023Article
- Lightweight neural network for smart diagnosis of cholangiocarcinoma using histopathological images.Scientific reports · 2023Article
- Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.Journal of medical ultrasonics (2001) · 2023Article
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
- Differential diagnosis of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on spatial and channel attention mechanisms.Journal of cancer research and clinical oncology · 2023Article
- Clinical applications of artificial intelligence in liver imaging.La Radiologia medica · 2023Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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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