Radiomics in hepatocellular carcinoma: A state-of-the-art review

Table 2 Some representative studies of deep learning in hepatocellular carcinoma

Ref.	Application task	Study design	Imaging modality	Algorithm	Sample size	Training set	Test/validation set	Performance
Bousabarah et al[91], 2021	Automatic detection and segmentation of HCC	Retrospective, single-center	CT	DCNN, U-net	174 patients with 231 lesions	165	33 (test); 33 (validation)	Mean DSC between automatically detected lesions using the DCNN + RF + TR and corresponding manual segmentations: 0.64/0.68 (validation/test), and 0.91/0.91 for liver segmentations
Yasaka et al[92], 2018	Differentiation of HCC and other liver tumors	Retrospective, single-center	CT	CNN	560 patients	460	100	Accuracy: 84% in test set
Hamm et al[93], 2019	Diagnosis and classification of HCC	Retrospective, single-center	MRI	CNN	494 patients	434	60	Accuracy: 92%, AUC: 0.992
Yamashita et al[95], 2020	Diagnosis and categorization of HCC with LI-RADS	Retrospective, multi-center	CT, MRI	CNN	314 patients (163 CT, 151 MRI)	220	47 (test); 47 (internal validation); 112 (external validation)	Overall accuracy: 60.4% and AUCs: 0.85, 0.90, 0.63, and 0.82 for LR-1/2, LR-3, LR-4, and LR-5, respectively
Wang et al[96], 2020	Preoperative prediction of MVI in HCC	Retrospective, single-center	MRI	CNN	97 patients with 100 HCCs	60 HCCs	40 HCCs	The combination of deep features from the b = 0, b = 100, b = 600, and ADC images presented the best results (AUC: 0.79)
Peng et al[97], 2020	Prediction of treatment response of TACE	Retrospective, multi-center (3 institutions)	CT	ResNet50	789 patients with HCC	562 (Institution 1)	89 (Institution 2); 138 (Institution 3)	Excellent predictive performance for CR, PR, SD, and PD (accuracy: 84.3%; AUCs: 0.97, 0.96, 0.95, and 0.96 in training set, accuracies: 85.1% and 82.8% in the two validation sets)
Zhang et al[98], 2020	Predicting OS after TACE + Sorafenib	Retrospective, multi-center (3 institutions)	CT	DenseNet (CNN)	201 patients with HCC	120 (Institutions 1 and 2)	81 (Institution 3)	Favorable prediction performance (C-index: 0.717 in training set, C-index: 0.714 in validation set)
Tamada et al[99], 2020	Motion artifact reduction	Retrospective, single-center	MRI	CNN	34 patients with HCC	14	20	Significant reduction of the magnitude of the artifacts and blurring induced by respiratory motion
Esses et al[100], 2018	Automated image quality evaluation	Retrospective, single-center	MRI	CNN	522 patients with HCC	351	171	High negative predictive value (94% and 86% relative to two readers)

DCNN: Deep convolutional neural network; TR: Thresholding; DSC: Dice similarity coefficient; CNN: Convolutional neural network; LI-RADS: Liver imaging reporting and data system; CR: Complete response; PR: Partial response; SD: Stable disease; PD: Progressive disease.