• Artificia intelligence
• Characterization
• Computed tomography (CT)
• Detection
• Diagnosis
• Focal liver lesions
• Magentic resonance imaging (MRI)

• Humans
• Artificial Intelligence
• Tomography, X-Ray Computed/methods
• Magnetic Resonance Imaging/methods
• Liver Neoplasms/diagnostic imaging
• Liver Diseases/diagnostic imaging
• Diagnosis, Differential
• Image Interpretation, Computer-Assisted/methods
• Deep Learning

• Artificial intelligence
• Classification
• Contrast-enhanced CT
• Deep learning
• Hepatic malignancy

• Tomography, X-Ray Computed
• Humans
• Image Processing, Computer-Assisted/methods
• Algorithms
• Time Factors

• Biomarkers
• Hepatic steatosis
• Liver cirrhosis
• Risk prediction

• AI models
• HCC
• artificial intelligence
• diagnostic performance
• hepatocellular carcinoma

• AI
• HCC
• liver tumors
• radiomics
• review

• LI-RADS
• MRI
• artificial intelligence
• liver cancer
• machine learning
• radiomics

• R01 CA195524 NCI NIH HHS
• the Dora Roberts Foundation

• Biomarkers
• Carcinoma, hepatocellular
• Deep learning
• Reproducibility of results
• Tomography, X-ray computed

• Humans
• Carcinoma, Hepatocellular/diagnostic imaging
• Deep Learning
• Reproducibility of Results
• Radiomics
• Liver Neoplasms/diagnostic imaging
• Algorithms
• Tomography, X-Ray Computed/methods
• Radiographic Image Interpretation, Computer-Assisted/methods

• NRF-2022R1F1A1072570 National Research Foundation of Korea

• Computed tomography (CT)
• Deep learning (DL)
• Hepatocellular carcinoma (HCC)
• The Liver Imaging Reporting and Data System (LI-RADS)
• Transformer

• Humans
• Carcinoma, Hepatocellular/pathology
• Liver Neoplasms/pathology
• Retrospective Studies
• Deep Learning
• Tomography, X-Ray Computed/methods
• Magnetic Resonance Imaging/methods
• Contrast Media
• Sensitivity and Specificity

• 2022ZDXM026 Chongqing medical scientific research project

• Male
• Humans
• Middle Aged
• Aged
• Retrospective Studies
• Deep Learning
• Radiology/education
• Radiologists
• Tomography, X-Ray Computed/methods
• Esophageal Neoplasms/diagnostic imaging

• Humans
• Liver Neoplasms/diagnostic imaging
• Carcinoma, Hepatocellular/diagnostic imaging
• Tomography, X-Ray Computed
• Neural Networks, Computer

• LI-RADS
• clinical study
• deep learning
• feature characterization
• lesion segmentation
• problem formulation

• Deep learning
• Hepatocellular carcinoma
• Liver
• Multidetector computed tomography

• Humans
• Aged
• Carcinoma, Hepatocellular/diagnostic imaging
• Carcinoma, Hepatocellular/pathology
• Liver/diagnostic imaging
• Observer Variation
• Deep Learning
• Liver Neoplasms/diagnostic imaging
• Liver Neoplasms/pathology
• Tomography, X-Ray
• Male
• Female
• Middle Aged
• Aged, 80 and over

• Japan Society for the Promotion of Science
• The University of Tokyo

• Artificial intelligence
• Hepatocellular carcinoma
• Precision medicine
• Radiological technology
• Radiomics

• CT
• Classification
• Deep learning
• LI-RADS
• MRI
• Radiomics

• Artificial Intelligence
• Carcinoma, Hepatocellular/pathology
• Humans
• Liver Neoplasms/pathology
• Magnetic Resonance Imaging/methods
• Retrospective Studies
• Sensitivity and Specificity

Hepatocellular carcinoma (HCC) is a unique cancer entity that can be noninvasively diagnosed using imaging modalities without pathologic confirmation. In 2018, several major guidelines for HCC were updated to include hepatobiliary contrast agent magnetic resonance imaging (HBA-MRI) and contrast-enhanced ultrasound (CEUS) as major imaging modalities for HCC diagnosis. HBA-MRI enables the achievement of high sensitivity in HCC detection using the hepatobiliary phase (HBP). CEUS is another imaging modality with real-time imaging capability, and it is reported to be useful as a second-line modality to increase sensitivity without losing specificity for HCC diagnosis. However, until now, there is an unsolved discrepancy among guidelines on whether to accept “HBP hypointensity” as a definite diagnostic criterion for HCC or include CEUS in the diagnostic algorithm for HCC diagnosis. Furthermore, there is variability in terminology and inconsistencies in the definition of imaging findings among guidelines; therefore, there is an unmet need for the development of a standardized lexicon. In this article, we review the performance and limitations of HBA-MRI and CEUS after guideline updates in 2018 and briefly introduce some future aspects of imaging-based HCC diagnosis.

• Contrast-enhanced ultrasound
• Diagnosis
• Guideline
• Hepatobiliary contrast agent
• Hepatocellular carcinoma

• Artificial intelligence
• Deep learning
• Hepatocellular carcinoma
• Medical imaging
• Predictive modeling
• Radiomics

• HCC
• LI-RADS
• MRI
• deep learning
• hepatocellular carcinoma
• liver imaging reporting and data system
• magnetic resonance imaging

• the Shanghai “Rising Stars of Medical Talent” Youth Development Program
• the National Natural Science Foundation of China

Liver cancer is a major cause of morbidity and mortality in the world. The primary goals of this manuscript are the identification of novel imaging markers (morphological, functional, and anatomical/textural), and development of a computer-aided diagnostic (CAD) system to accurately detect and grade liver tumors non-invasively. A total of 95 patients with liver tumors (M = 65, F = 30, age range = 34–82 years) were enrolled in the study after consents were obtained. 38 patients had benign tumors (LR1 = 19 and LR2 = 19), 19 patients had intermediate tumors (LR3), and 38 patients had hepatocellular carcinoma (HCC) malignant tumors (LR4 = 19 and LR5 = 19). A multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) was collected to extract the imaging markers. A comprehensive CAD system was developed, which includes the following main steps: i) estimation of morphological markers using a new parametric spherical harmonic model, ii) estimation of textural markers using a novel rotation invariant gray-level co-occurrence matrix (GLCM) and gray-level run-length matrix (GLRLM) models, and iii) calculation of the functional markers by estimating the wash-in/wash-out slopes, which enable quantification of the enhancement characteristics across different CE-MR phases. These markers were subsequently processed using a two-stages random forest-based classifier to classify the liver tumor as benign, intermediate, or malignant and determine the corresponding grade (LR1, LR2, LR3, LR4, or LR5). The overall CAD system using all the identified imaging markers achieved a sensitivity of 91.8%±0.9%, specificity of 91.2%±1.9%, and F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{1}$$\end{document}1 score of 0.91±0.01, using the leave-one-subject-out (LOSO) cross-validation approach. Importantly, the CAD system achieved overall accuracies of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$88\%\pm 5\%$$\end{document}88%±5%, 85%±2%, 78%±3%, 83%±4%, and 79%±3% in grading liver tumors into LR1, LR2, LR3, LR4, and LR5, respectively. In addition to LOSO, the developed CAD system was tested using randomly stratified 10-fold and 5-fold cross-validation approaches. Alternative classification algorithms, including support vector machine, naive Bayes classifier, k-nearest neighbors, and linear discriminant analysis all produced inferior results compared to the proposed two stage random forest classification model. These experiments demonstrate the feasibility of the proposed CAD system as a novel tool to objectively assess liver tumors based on the new comprehensive imaging markers. The identified imaging markers and CAD system can be used as a non-invasive diagnostic tool for early and accurate detection and grading of liver cancer.

• LI-RADS
• computed tomography
• hepatocellular carcinoma
• magnetic resonance imaging

• Artificial intelligence
• Dual-energy CT
• Hepatocellular carcinoma
• Perfusion CT
• Tomography, X-ray computed

• Carcinoma, Hepatocellular/diagnosis
• Humans
• Liver/diagnostic imaging
• Liver Neoplasms/diagnosis
• Tomography, X-Ray Computed/methods

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

• Aged
• Carcinoma, Hepatocellular/classification
• Carcinoma, Hepatocellular/diagnosis
• Cross-Sectional Studies
• Female
• Humans
• Liver Neoplasms/classification
• Liver Neoplasms/diagnosis
• Male
• Middle Aged
• Neural Networks, Computer
• Pilot Projects
• Retrospective Studies
• Tomography, X-Ray Computed/methods

• Artificial neuronal network
• Deep learning
• Hepatocellular cancer
• Liver transplantation
• Recurrence
• Resection

• Artificial Intelligence
• Carcinoma, Hepatocellular/therapy
• Humans
• Liver Neoplasms/therapy
• Neoplasm Recurrence, Local
• Prognosis
• Reproducibility of Results

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results.

Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists.

Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914–0.979 vs. 0.951; 0.919–0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960–1.000), metastatic tumors (0.998; 0.989–1.000), and other primary malignancies (0.963; 0.896–1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists.

Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.

• MRI
• artificial intelligence
• deep learning
• diagnosis
• liver cancer
• liver mass