Copyright
©The Author(s) 2021.
World J Gastroenterol. Oct 28, 2021; 27(40): 6825-6843
Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6825
Published online Oct 28, 2021. doi: 10.3748/wjg.v27.i40.6825
Ref. | Task | Method | MR image | Accuracy | Sensitivity | Specificity | AUROC |
Hectors et al[60], 2020 | Stage liver fibrosis | VGG16 CNN | Gadoxetic acid-enhanced MRI | F1-4: 0.69 | F1-4: 0.64 | F1-4: 0.90 | F1-4: 0.77 |
F2-4: 0.85 | F2-4: 0.82 | F2-4: 0.93 | F2-4: 0.91 | ||||
F3-4: 0.85 | F3-4: 0.87 | F3-4: 0.83 | F3-4: 0.90 | ||||
F4: 0.78 | F4: 0.73 | F4: 0.81 | F4: 0.85 | ||||
Liu et al[55], 2021 | Classify cHCC-CC vs non-cHCC-CC and HCC vs non-HCC | Radiomics + SVM | Gadoxetic acid-enhanced MRI | cHCC-CC vs non-cHCC-CC: 0.77 | cHCC-CC vs non-cHCC-CC: 0.65 | cHCC-CC vs non-cHCC-CC: 0.81 | cHCC-CC vs non-cHCC-CC: 0.77 |
HCC vs non-HCC: - | HCC vs non-HCC: 0.68 | HCC vs non-HCC: 0.88 | HCC vs non-HCC: 0.79 | ||||
Wu et al[48], 2020 | Classify tumours according to their LI-RADS grade | AlexNet CNN | Gadoxetic acid-enhanced MRI | 0.9 | 1 | 0.835 | 0.95 |
Messaoudi et al[50], 2020 | Classify tumours into HCC or non-HCC | Patch based CNN | Multiphase 3D fast spoiled gradient echo T1 | 0.9 | ? | ? | ? |
Hamm et al[51], 2019 | Classify tumours into type and LI-RADS derived classes | CNN | Multiphase contrast-enhanced T1-weighted MRI | Lesion class: 0.919 | Lesion class: 0.90 | Lesion class: 0.98 | LI-RADS (HCC): 0.922 |
LI-RADS: 0.943 | LI-RADS: 0.92 | LI-RADS: 0.97 | |||||
Trivizakis et al[54], 2018 | Classify tumours into primary or metastatic | 3D CNN + SVM | Diffusion weighted MRI | 0.83 | 0.93 | 0.67 | 0.8 |
He et al[65], 2019 | Correctly predict liver stiffness using clinical and radiomic data | Radiomics + SVM | T2-weighted MRI | 0.818 | 0.722 | 0.87 | 0.84 |
Schawkat et al[61], 2020 | Stage liver fibrosis into low-stage (F0-2) and high-stage (F3-4) | Radiomics + SVM | T1-weighted MRI, T2-weighted MRI | T1-w: 0.857 | ? | ? | T1-w: 0.82 |
T2-w: 0.57 | |||||||
T2-w: 0.619 | |||||||
Lewis et al[56], 2019 | Distinguish HCC from other primary cancers | Radiomics + Binary logistic regression | Diffusion weighted MRI | Observer 1: 0.815 | Observer 1: 0.793 | Observer 1: 0.889 | Observer 1: 0.90 |
Observer 2: 0.80 | Observer 2: 0.862 | Observer 2: 0.778 | Observer 2: 0.89 | ||||
Wu et al[57], 2019 | Classify tumours into HCC and HH | Radiomics + logistic regression | T2-weighted MRI, Diffusion weighted MRI, T1-weighted GRE in phase and out of phase MRI | ? | 0.822 | 0.714 | 0.89 |
Oyama et al[58], 2019 | Classification of hepatic tumours into HCC, HH and MT | Radiomics + logistic regression/XGBoost | T1-weighted MRI | HCC vs MT: 0.92 | HCC vs MT: 1.0 | HCC vs MT: 0.84 | HCC vs MT: 0.95 |
HCC vs HH: 0.9 | HCC vs HH: 0.96 | HCC vs HH: 0.84 | HCC vs HH: 0.95 | ||||
MT vs HH: 0.73 | MT vs HH: 0.72 | MT vs HH: 0.74 | MT vs HH: 0.75 | ||||
Wu et al[59], 2019 | Predict pre-operative HCC grade | Combined clinical data and Radiomics + logistic regression | T2/T1-weighted | 0.761 | 0.85 | 0.65 | 0.8 |
Chen et al[69], 2019 | Predict pre-treatment immunscore in HCC | Combined clinical data and radiomics + multi-vote decision trees | Gadoxetic acid-enhanced MRI | 0.842 | 0.846 | 0.841 | 0.934 |
Park et al[63], 2019 | Stage liver fibrosis | Radiomics + logistic regression | Gadoxetic acid-enhanced MRI | F2-4: 0.803 | F2-4: 0.814 | F2-4: 0.784 | F2-4: 0.91 |
F3-4: 0.803 | F3-4: 0.789 | F3-4: 0.820 | F3-4: 0.88 | ||||
F4: 0.813 | F4: 0.921 | F4: 0.754 | F4: 0.87 | ||||
Zhao et al[67], 2019 | Predict early reoccurrence of IMCC | Combined clinical data and radiomics + logistic regression | T2-weighted MRI, gadoxetic acid-enhanced MRI | 0.872 | 0.938 | 0.839 | 0.949 |
Reimer et al[68], 2018 | Predict therapy response to transarterial radioembolization | Radiomics + logistic regression | Gadoxetic acid-enhanced MRI | ? | Arterial phase: 0.83 | Arterial phase: 0.62 | Arterial phase: 0.73 |
Venous phase: 0.71 | Venous phase: 0.85 | Venous phase: 0.76 | |||||
Zhen et al[53], 2020 | Classify liver tumours into benign , HCC, metastatic or other primary malignancy | CNN with clinical input | T2, diffusion, Pre- contrast T1, late arterial, portal venous, and equilibrium phase | 0.919 | HCC: 0.957 | HCC: 0.904 | HCC: 0.951 |
Metastatic: 0.946 | Metastatic: 1.0 | Metastatic: 0.985 | |||||
Other primary: 0.733 | Other primary: 0.964 | Other primary: 0.989 | |||||
Yasaka et al[62], 2017 | Stage liver fibrosis | CNN | Gadoxetic acid-enhanced MRI | F4 vs F3-0: 0.75 | F4 vs F3-0: 0.76 | F4 vs F3-0: 0.76 | F4 vs F3-0: 0.84 |
F4-3 vs F2-0: 0.77 | F4-3 vs F2-0: 0.78 | F4-3 vs F2-0: 0.74 | F4-3 vs F2-0: 0.84 | ||||
F4-2 vs F1-0: 0.80 | F4-2 vs F1-0: 0.84 | F4-2 vs F1-0: 0.65 | F4-2 vs F1-0: 0.84 | ||||
Kim et al[70], 2019 | Predict postoperative early and late recurrence of single HCC | Radiomics + random forests | Gadoxetic acid-enhanced MRI | Harrel C-statistic: 0.716 in combined radiomic and clinicopathologic model, no significant difference to clinicopathologic model (0.696) | |||
Kim et al[52], 2020 | Detect HCC | CNN | Gadoxetic acid-enhanced MRI | 0.937 | 0.94 | 0.99 | 0.97 |
Liu et al[71], 2020 | Identify clinically significant portal hypertension | CNN + logistic regression | ? | ? | 0.929 | 0.846 | 0.94 |
- Citation: Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27(40): 6825-6843
- URL: https://www.wjgnet.com/1007-9327/full/v27/i40/6825.htm
- DOI: https://dx.doi.org/10.3748/wjg.v27.i40.6825