Minireviews
Copyright ©The Author(s) 2021.
World J Gastroenterol. Sep 14, 2021; 27(34): 5715-5726
Published online Sep 14, 2021. doi: 10.3748/wjg.v27.i34.5715
Table 2 Hepatitis or hepatitis associated lesion detection based on radiology
No.
Task
Algorithms (model)
Sample size (type)
Evaluation index
Ref.
1Predicting clinical severity in AAH patientsRandom forest; Convolutional neural network69 cases (CT texture features)Accuracy: 82.4% of RFE-RF in the test set; Accuracy: 70% of CNN in the test set[26]
2Assessing significant liver fibrosis by multiparametric ultrasomics dataAdaboost; Random forest; SVM (multiparametric ultrasomics)144 HBV infected patients (multiparametric ultrasomics)AUROC: 0.85 ± 0.01 of Adaboost, random forest, SVM in multiparametric ultrasomics including conventional ultrasomics, ORF and CEMF[9]
3Grading liver fibrosisInception-V3 network (transfer learning) 466 patients (multimodal ultrasound)AUCs of TL in GM + EM reached 0.950, 0.932, and 0.930, respectively, for grading S4, ≥ S3, and ≥ S2an[28]
4Predicting cirrhosisLASSO (radiomics nomogram)144 cases of HBV patients (CT features and clinical factors)AUROC: 0.915 in the training cohort, 0.872 in the validation cohort, overall correctly classified rate of 82.0%[29]
5Differentiating hepatic fibrosis’ gradeRFC (CTTA-based models); SVM (CTTA-based models)30 fibrosis patients (CT texture features)Train AUC 0.95 in RFC (model 1); Test AUC 0.90 in RFC (model 1); Train AUC 0.88 in SVM (model 2); Test AUC 0.76 in SVM (model 2)[30]
6Assessing liver fibrosis severityA prototype convolutional neural network558 cases (CT images)AUCs were 0.82, 0.85, and 0.88 of VolL/VolS in diagnosing advanced fibrosis, cirrhosis, and decompensated cirrhosis in the whole study population[31]
7Staging liver fibrosisConvolutional neural network 634 fibrosis patients (MR images and MR/virus)AUCs were 0.84, 0.84, and 0.85 of the model full for diagnosing F4, ≥ F3, and ≥ F2 in the test set, respectively[34]
8Assessing liver fibrosis in chronic hepatitis BConvolution neural network (DLRE)398 HBV patients (shear wave elastography)AUCs of DLRE 1.00, 0.99, and 0.99 for classifying F4, ≥ F3, and ≥ F2 in the training set and 0.97, 0.98, and 0.85 in the validation set[35]
9Diagnosing FNH from HCC in the non-cirrhotic liverLASSO (radiomics nomogram)156 patients (CT images and clinical factors)Accuracy: 92.4% in the training set, 89.2% in the validation set[38]
10Diagnosing HCCLASSO (radiomics signature)211 patients (MR images)AUROC: 0.861 in the training set, 0.810 in the validation set[39]
11Preoperative prediction of HCC gradeLASSO (combined model with clinical factors and radiomics signature)170 HCC patients (MR images and clinical factors)AUROC: 0.742, 0.786, and 0.800 based on T1WI images, T2WI images, and combined T1WI and T2WI images in the combined model[41]
12Predicting MVI risk in HBV-related HCC preoperativelyLASSO (radiomics nomogram)304 HCC patients (CT images and AFP)AUROC: 0.846 in the training set, 0.844 in the validation set[43]
13Preoperative prediction of MVI in HCC patients LASSO (combined model)157 HCC patients (CT images and clinical factors)AUROC: 0.835 in the training dataset, 0.801 in the validation dataset[44]
14Predicting risk of HE complicated by hepatitis B related cirrhosisLASSO (integrated model of radiomics and clinical features)304 cirrhosis patients (CT images and clinical factors)Accuracy: 0.93 in the training cohort, 0.83 in the testing cohort[45]
15Predicting liver failure in cirrhotic patients with HCC after major hepatectomyLASSO (integrated radiomics-based mode)101 HCC patients (MR images and clinical factors)Accuracy: 0.802 in radiomics-based model[47]