Multimodal Ultrasound Radiomics in Liver Disease: Current Status and Future Directions

doi:10.26599/AUDT.2025.250101

Abstract

Abstract:

Multimodal ultrasound, including B-mode imaging, contrast-enhanced ultrasound (CEUS), and ultrasound-based elastography, has demonstrated significant value in evaluating both diffuse liver diseases such as fibrosis and steatosis, and focal liver lesions such as hepatocellular carcinoma (HCC). Radiomics, including both handcrafted radiomics and deep learning approaches, has emerged as a promising strategy to enhance ultrasound-based liver disease assessment. Recent studies have applied radiomics across multimodal ultrasound, achieving notable success in grading fatty liver disease, staging fibrosis, and improving diagnosis, risk stratification, and prognostic prediction in HCC. Multimodal ultrasound provides complementary information on liver morphology, perfusion, and stiffness, while fusion strategies further enhance diagnostic accuracy and robustness. Future efforts should focus on standardized, large-scale multicenter validation, methodological improvements in multimodal integration, and the incorporation of explainable artificial intelligence to support clinical translation. Ultimately, despite ongoing challenges related to data heterogeneity, reproducibility, interpretability, and clinical validation, multimodal ultrasound radiomics holds strong promise for noninvasive, individualized, and clinically meaningful liver disease management.

Key words: Handcrafted radiomics; Deep learning; Contrast-enhanced ultrasound; Shear wave elastography; Liver disease

Zhong Xian, Xie Xiaoyan. Multimodal Ultrasound Radiomics in Liver Disease: Current Status and Future Directions. Advanced Ultrasound in Diagnosis and Therapy, 2025, 9(4): 388-408.

Figures/Tables 7

Figure 1

Figure 2

Table 1

Summary of published radiomics studies on fatty liver disease"

Reference	Study design	Imaging modality	No. of patients/ images	Radiomics technique	Detailed method	Task	Reference standard	Results
Reference	Study design	Imaging modality	No. of patients/ images	Radiomics technique	Detailed method	Task	Reference standard	AUC	Accuracy	Sensitivity	Specificity
HR, handcrafted radiomics; DL, deep learning; FLD, fatty liver disease; CAP, controlled attenuation parameter; CNN, convolutional neural networks; MRI-PDFF, magnetic resonance imaging derived proton density fat fraction; ORF, original radio frequency signal; CDFI, Color doppler flow imaging
Wu 2022 [38]	Retrospective, single center	B-mode	321 images from 235 patients	HR	Different classifiers	FLD diagnosis	CAP and biopsy	0.75	70	66.4	NA
Cao 2020 [33]	Retrospective, single center	B-mode	240 images from 240 patients	DL	CNN	FLD diagnosis and severity classification	US evaluation by radiologists	0.933-0.958
Byra 2018 [40]	Retrospective, single center	B-mode	550 images from 55 patients	DL	Inception-ResNet-v2+SVM	FLD diagnosis	Liver biopsy	0.977	96.3	100	88.2
Reddy 2018 [41]	Retrospective, single center	B-mode	157 images	DL	VGG16 Transfer Learning	FLD diagnosis	US evaluation by radiologists	0.96	90.6	95	85
Kim 2021 [42]	Retrospective, single center	B-mode	180 images from 90 cases	DL	VGG19	FLD diagnosis	MRI–PDFF	0.87	80.1	NA	80.5
Han 2020 [43]	Prospective, single center	ORF	204 patients	DL	One-dimensional CNN	FLD diagnosis and fat fraction estimation	MRI–PDFF	0.98	96	97	94
Chou 2021 [44]	Retrospective, single center	B-mode	21855 images from 2070 patients	DL	ResNet-50 v2	FLD diagnosis and severity classification	US evaluation by radiologists	0.971-0.996
Tahmasebi 2023 [45]	Prospective, single center	B-mode	1435 images from 130 patients	DL	Google AutoML	FLD diagnosis	MRI–PDFF	NA	83.4	72.2	94.6
Rhyou 2021 [21]	Retrospective, multicenter	B-mode	3200 images from 1902 patients'	DL	Cascaded Neural Network	FLD diagnosis and severity classification	US evaluation by radiologists	NA	99.6	99.1	100
Yang 2023 [46]	Retrospective, single center	B-mode	1856 images from 928 patients	DL	Two-section neural network	FLD diagnosis and severity classification	US evaluation by radiologists	0.84-0.93	76.3
Liu 2024 [47]	Prospective, single center	B-mode +CDFI	710 images from 710 patients	DL+HR	VGG16 model and two HR features	FLD diagnosis and severity classification	NA	0.836-0.947	77.5

Table 1

Table 2

Summary of published ultrasound radiomics studies for diagnosis and grading of liver fibrosis"

Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	Results
Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	AUC	Accuracy	Sensitivity	Specificity
HR, handcrafted radiomics; DL, deep learning; TE, transient elastography; CNN, convolutional neural networks; GAN, generative adversarial network; ORF, original radio frequency signal; SWE, shear wave elastography; SVM, support vector machines; LSM, liver stiffness measurement; STQ, sound touch quantification; STE, sound touch elastography
Meng 2017 [48]	Retrospective, single center	B-mode	279 patients	DL	Transfer Learning and FCNet	Liver fibrosis classification	NA		93.9
Lee 2020 [49]	Retrospective, single center	B-mode	3446 patients	DL	DCNN	Liver fibrosis classification	Pathology or TE	0.857 for cirrhosis prediction	88.3	77.8	93.7
Feng 2021 [50]	Retrospective, single center	B-mode	286 patients	DL	Pyramid-structured CNN	Liver fibrosis classification	Liver biopsy	0.99	95.7	96.5
Ruan 2021 [51]	Retrospective, multicenter	B-mode	508 patients	DL	Multi-scale texture network (MSTNet)	Liver fibrosis classification	Liver biopsy	0.92 (≥F2) 0.89 (F4)		85.1 (≥F2) 87.8 (F4)	87.6 (≥F2) 78.1 (F4)
Duan 2022 [52]	Retrospective, multicenter	B-mode	434 patients	DL+HR	DL features generated by GAN and HR features	Liver fibrosis screening	Liver biopsy	0.861 for cirrhosis prediction
Joo 2023 [53]	Retrospective, multicenter	B-mode	955 patients	DL	Transfer learning of different models	Liver fibrosis classification	Liver biopsy	0.8592
Park 2024 [54]	Retrospective, multicenter	B-mode	933 patients	DL	EfficientNet	Liver fibrosis classification	Liver surgery and biopsy	0.94-0.96 for F0-F4	93-96	79-89	95-98
Ai 2024 [55]	Retrospective, single center	ORF	237 patients	DL	2D CNN for segmentation and 1D CNN for classification	Liver fibrosis classification	Liver biopsy	0.957 (≥F1) 0.729 (≥F3) 0.876 (≥F4)
Zhang 2025 [56]	Retrospective, multicenter	high-frequency B-mode images	1500 patients	DL	InceptionNeXt	Liver fibrosis classification	Liver biopsy	0.81 (≥S2) 0.93 (≥S3) 0.87 (S4)	74 (≥S2) 85 (≥S3) 84 (S4)	84 (≥S2) 85 (≥S3) 79 (S4)	65 (≥S2) 86 (≥S3) 85 (S4)
Chen 2017 [57]	Retrospective, multicenter	Real-time tissue elastography	513 patients	HR	Random Forest	Liver fibrosis classification	Liver biopsy		82	75	86
Gatos 2016 [58]	Retrospective, single center	SWE	85 patients	HR	Color to stiffness mapping and SVM	Liver fibrosis classification	Liver biopsy	0.85	87	83.3	89.1
Gatos 2017 [59]	Retrospective, single center	SWE	126 patients	HR	Stiffness value clustering and SVM	Liver fibrosis classification	Liver biopsy	0.87	87.3	93.5	81.2
Gatos 2019 [60]	Retrospective, single center	SWE	200 patients	DL	A wavelet transform and fuzzy c-means clustering algorithm to detect areas of high and low stiffness temporal stability	Liver fibrosis classification	Liver biopsy	0.89-0.95	82.5-92.5
Kagadis 2020 [61]	Retrospective, single center	SWE	200 patients	DL	Transfer learning of different models	Liver fibrosis classification	Liver biopsy	0.979-0.990	87.2-97.4
Wang 2018 [33]	Prospective, multicenter	SWE	398 patients	DL	Deep learning radiomics of elastography (DLRE)	Liver fibrosis classification	Liver biopsy	0.85 (≥F2) 0.98 (≥F3) 0.97 (F4)		69.1 (≥F2) 90.4 (≥F3) 96.9 (F4)	90.9 (≥F2) 98.3 (≥F3) 88.0 (F4)
Meng 2023 [62]	Retrospective, single center	B-mode, SWE, clinical parameters	618 patients	HR	SVM	Predicting the risk of fibrosis Progression NAFLD	LSM values	0.95		86.2
Li 2019 [35]	Prospective, single center	B-mode, ORF and contrast-enhanced micro-flow (CEMF)	144 patients	HR	Multiparametric model	Diagnosis of significant liver fibrosis	Liver surgery and biopsy	0.78-0.85		87.5-93.8	69.2-76.9
Liu 2022 [63]	Retrospective, single center	B-mode, liver stiffness values, and clinical parameters	284 patients	DL	deep learning-based data integration network (DI-Net)	Diagnosis of significant liver fibrosis	Liver surgery and biopsy	0.901	81.3	81.6	80.8
Liu 2023 [34]	Retrospective, single center	B-mode, Contrast-enhanced microflow (CEMF) cines and clinical parameters	218 patients	DL	Data integration-based deep learning (DIDL)	Diagnosis of significant liver fibrosis	Liver surgery and biopsy	0.901	81.3	81.6	80.9
Gao 2021 [64]	Retrospective, single center	B-mode, STQ, STE	168 patients	DL	Multi-modal fusion network with AL (MMFN-AL)	Liver fibrosis classification	Liver biopsy	0.891	70.59
Xue 2020 [8]	Retrospective, multicenter	B-mode, SWE	466 patients	DL	Transfer learning of Inception-V3	Liver fibrosis classification	Liver surgery	0.93 (≥S2) 0.93 (≥S3) 0.95 (S4)		90.0 (≥S2) 89.9 (≥S3) 90.1 (S4)	87.8 (≥S2) 87.9 (≥S3) 94.3 (S4)
Lu 2021 [65]	Retrospective, multicenter	B-mode, SWE, clinical parameters	807 patients	DL	Multichannel deep learning radiomics models (DLRE 2.0)	Diagnosis of significant liver fibrosis	Liver biopsy	0.95		90.6	90.1
Chen 2024 [66]	Retrospective, multicenter	B-mode, SWE	5894 patients	DL	ResNet152 for liver stiffness prediction and sequential algorithm for liver fibrosis screening	Liver fibrosis screening	Liver biopsy		85	54	94

Table 2

Table 3

Summary of published ultrasound radiomics studies for diagnosis of focal liver lesions"

Reference	Study design	Imaging modality	No. of patients/ images	Radiomics technique	Detailed method	Task	Reference standard	Results
Reference	Study design	Imaging modality	No. of patients/ images	Radiomics technique	Detailed method	Task	Reference standard	AUC	Accuracy	Sensitivity	Specificity
HR, handcrafted radiomics; DL, deep learning; FLL, focal liver lesions; HCC, hepatocellular carcinoma; ICC, intrahepatic cholangiocarcinoma; FFS, focal fat sparing; FFI, focal fat infiltration; MLC, metastatic liver cancer; TIC, time-intensity curve; CEUS, contrast-enhanced ultrasound; CNN, convolutional neural networks; FNH, focal nodular hyperplasia; SWE, shear wave elastography; SWV, shear wave velocity
Hwang 2015 [67]	Retrospective, NA	B-mode	115 patients	HR	HR features with artificial neural network	FLL diagnosis (cysts, hemangiomas, malignancies)	NA
Mao 2021 [68]	Retrospective, single center	B-mode	114 patients	HR	Different classifiers	Classification of primary and metastatic liver cancer	Pathology	0.816	84.3	76.8	88
Peng 2020 [69]	Retrospective, single center	B-mode and clinical variables	531 patients	HR	Different classifiers	Differentiation among HCC and ICC/cHCC-ICC	Pathology	0.728-0.775
Qin 2020 [70]	Retrospective, single center	B-mode	254 patients	HR	Different classifiers	Identification of primary tumorous sources of liver metastases	Pathology	0.750-0.768	70.6; 79.2; 64.3	73.9; 70.0; 75.0	67.9; 85.7; 50.0
Peng 2022 [71]	Retrospective, single center	B-mode	589 patients	HR	Different classifiers	Differentiating infected focal liver lesions from malignancy	Pathology or follow up/imaging	0.745-0.836	66.7-78.4	65.4-87.7	45.2-67.7
Schmauch 2019 [72]	NA,	B-mode	544 patients	DL	algorithm was trained using an attention with annotations	FLL diagnosis (benign and malignant)	NA	0.812-0.922
Xi 2021 [73]	Retrospective, single center	B-mode	596 patients	DL	Resnet	FLL diagnosis (benign and malignant)	MRI or histopathology	0.85	80	91	62
Tiyarattanachai 2021 [74]	Retrospective, multicenter	B-mode	22472 lesions	DL	RetinaNet for both detection and diagnosis	FLL detection and diagnosis (HCC, cyst, hemangioma, FFS and FFI)	Pathology and/or MRI/CT		95.3	84.9	97.1
Chen 2024 [75]	Retrospective, single center	B-mode	465 patients	DL	Different DCNN	Differentiation among HCC, ICC, and cHCC-ICC	Pathology	0.92	86	84.59	92.65
Yang 2020 [76]	Retrospective, multicenter	B-mode, clinical variables	2143 patients	DL	Resnet 18	FLL diagnosis (benign and malignant)	Pathology	0.924	84.7	86.5	85.5
Yang 2023 [77]	Retrospective, multicenter	B-mode	6784 patients	DL	Resnet 50	Identification of hepatic echinococcosis	Pathological or clinical diagnosis	0.913-0.982 (echinococcosis) 0.900-0.986 (alveolar echinococcosis)	71.9-84; 90.7-94.6	92.1-100; 77.1-97.1	66.6-80.5; 92.4-94.2
Du 2025 [78]	Retrospective, multicenter	B-mode and clinical parameters	1052 patients	HR	XGBoost	classification of ≤3 cm HCC	Pathology or CT/MRI with follow-up	0.899	85.9	92.8	77.9
Streba 2012 [79]	Prospective, single center	CEUS	112 patients	HR and DL	TIC features	FLL diagnosis (HCC, MLC, hepatic hemangiomas, local fatty changes)	Pathology or CT/MRI or follow up	0.89	87.1	93.2	89.7
Gatos 2015 [80]	Retrospective, single center	CEUS	52 patients	HR	TIC features	FLL detection and diagnosis (benign and malignant)	Pathology or CT/MRI	0.89	90.3	93.1	86.9
Kondo 2017 [81]	Retrospective, single center	CEUS	98 patients	HR	TIC features	FLL diagnosis (benign and malignant, differentiation of benign, HCC, or MLC)	Pathology or CT/MRI with follow-up		91.8	94	87.1
Turco 2022 [82]	Retrospective, single center	CEUS	72 patients	HR	TIC features and textural features	FLL diagnosis (benign and malignant)	Pathology and/or MRI/CT	0.84	84	76	92
Guo 2018 [83]	Retrospective, single center	CEUS	93 patients	HR	Deep canonical correlation analysis (DCCA) and multiple kernel learning (MKL)	FLL diagnosis (benign and malignant)	Pathology or CT/MRI		90.4	93.6	86.9
Li 2024 [84]	Retrospective, single center	CEUS, clinical features	159 patients	HR	Textual features from 6 CEUS images	Differentiation among HCC and ICC/cHCC-ICC in LR-M patients	Pathology	0.912	85.4	95.2	77.8
Wang 2024 [85]	Prospective, multicenter	CEUS, clinical features	116 patients	HR	HR features from one Kupffer phase image	Differentiating well-differentiated hepatocellular carcinoma (w-HCC) from atypical FLL	Pathology	0.912		82.4	85
Pan 2019 [86]	Retrospective, single center	CEUS	242 tumors	DL	3D-CNN	FLL diagnosis (HCC and FNH)	Pathology	0.97	93.1	94.5	93.6
Caleanu 2021 [87]	Retrospective, single center	CEUS	91 patients	DL	Different DCNN	FLL diagnosis (HCC, MLC, hemangiomas, FNH)	NA		88
Ding 2025 [88]	Retrospective, multicenter	CEUS	3725 lesions	DL	Module-Disease, Module-Biomarker and Module-Clinic were built and aggregated	FLL diagnosis (HCC, MLC, ICC, hepatic hemangioma, hepatic abscess and others)	Pathology for malignancy or MRI with follow-up for benign lesions	0.86		85	97
Yao 2018 [89]	Retrospective, single center	B-mode, SWE, SWV	177 patients	HR	Sparse representation theory (SRT)	FLL diagnosis (benign and malignant)	Pathology	0.94	88	91	86
Hu 2024 [90]	Retrospective, single center	B-mode, CEUS	527 patients	HR	HR features extracted from both B-mode images and three-phase CEUS images	FLL diagnosis (benign and malignant)	Pathology for malignant lesions; CEUS and follow-up for benign lesions	0.914		95.2	72.2
Su 2024 [91]	Retrospective, single center	B-mode, CEUS, clinical features	280 patients	HR	HR features from both B-mode and CEUS images	Differentiation of HCC and ICC	Pathology	0.97	91	89	93
Hu 2021 [92]	Retrospective, single center	B-mode, CEUS	574 patients	DL	ResNet on four-phase images	FLL diagnosis (benign and malignant)	Pathology for malignant lesions; CEUS and follow-up for benign lesions	0.934	91	92.7	85.1
Liu 2022 [93]	Retrospective, multicenter	B-mode, CEUS, clinical features	303 patients	DL	Four Stream (B-mode, CEUS and their optic flow) 3D convolutional neural network	FLL diagnosis (benign and malignant)	Pathology	0.957	94	96.6	90.5

Table 3

Table 4

Summary of published ultrasound radiomics studies for risk prediction of hepatocellular carcinoma"

Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	Results
Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	AUC	Accuracy	Sensitivity	Specificity
ORF, original radio frequency signal; HR, handcrafted radiomics; DL, deep learning; CEUS, contrast-enhanced ultrasound; MVI, microvascular invasion; CNN, convolutional neural networks; SWE, shear wave elastography
Dong 2019 [94]	Prospective, single center	ORF	42 patients	HR	Signal analysis and processing technology in feature extraction	MVI prediction	Pathology	0.95	92.8	85.7	100
Dong 2020 [95]	Retrospective, single center	B-mode, clinical variables	322 patients	HR	Gross- and peri-tumoral region HR	MVI prediction	Pathology	0.744	63.4	89.2	48.4
Hu 2019 [96]	Retrospective, single center	B-mode, clinical variables	482 patients	HR	Radiomics features combined with clinical variables	MVI prediction	Pathology	0.731
Dong 2022 [97]	Prospective, single center	CEUS, clinical variables	100 patients	HR	Radiomics features from peritumoral liver tissues of Kupffer phase	MVI prediction	Pathology	0.804	75	87.5	69.1
Zhang 2021 [98]	Retrospective, single center	B-mode, CEUS, clinical variables	313 patients	HR	HR features from different CEUS phase	MVI prediction	Pathology	0.788	72.7	75.5	70.8
Zhang 2022 [99]	Retrospective, single center	CEUS, clinical variables	436 patients	DL	GRU for temporal features and CNN for spatial features	MVI prediction	Pathology	0.865	78.8	83.3	81
Qin 2023 [100]	Retrospective, single center	CEUS	252 patients	DL	ResNet50+SE	MVI prediction	Pathology	0.856	77.2	52.4	93.9
Qin 2025 [101]	Retrospective, single center	CEUS	164 patients	DL	Transformer+Resnet	MVI prediction	Pathology	0.859	71.4	86.2	80
Wang 2025 [102]	Retrospective, single center	CEUS	318 patients	DL	Graph Convolutional Network	MVI prediction	Pathology	0.928	89.3	85.3	91.7
Zheng 2023 [103]	Retrospective, single center	CEUS, MRI	85 patients	HR	Comparison between CEUS and MRI radiomics model	MVI prediction	Pathology	0.86		100	85.7
Zhang 2024 [104]	Retrospective, multicenter	B-mode, CEUS, clinical variables	576 patients	HR, DL	Comparison of DL and HR models with different conditions	MVI prediction	Pathology	0.738	71	60.6	76.5
Li 2025 [105]	Experiments on rabbits	3D ultrasound	9 rabbits	HR	3D US images are fused with whole-slide images to localize MVI regions	MVI prediction	Pathology	0.91	86	76	92
Ren 2021 [106]	Retrospective, multicenter	B-mode, clinical variables	193 patients	HR	combination of HR features and clinical variables	Differentiation prediction	Pathology	0.849	81.8	75	85.7
Qin 2023 [107]	Retrospective, single center	CEUS	272 patients	DL+HR	Combination of both DL and HR features	Differentiation prediction	Pathology	0.932	91.5	93.8	90
Li 2022 [108]	Retrospective, single center	CEUS	54 patients	HR	Radiomics features from Kupffer phase	Differentiation prediction	Pathology	0.878-0.938	97.1 97.1	93.3 83.3	98.1 100
Qian 2023 [109]	Retrospective, single center	B-mode	118 patients	HR	HR features from intratumoral and peritumoral regions	Ki-67 prediction	Pathology	0.87	80.6	73.7	88.2
Zhang 2024 [110]	Retrospective, single center	B-mode, CEUS, clinical variables	310 patients	HR	HR features from different CEUS phase	Ki-67 prediction	Pathology	0.856	76.8	79.3	74.1
Yao 2018 [89]	Retrospective, single center	B-mode, SWE, SWV	177 patients	HR	Sparse representation theory (SRT)	PD-1, Ki-67, MVI prediction	Pathology	0.94-0.98	92; 93; 95	100; 95; 91	88; 89; 100
Qian 2024 [111]	Retrospective, single center	B-mode	153 patients	HR	Different classifiers	Prediction of differentiation, CK-7, Ki-67 and p53	Pathology	0.762-0.922	82.1; 87.5; 75.0; 70.3	78.2; 100; 85.7 100
Bu 2025 [112]	Retrospective, single center	B-mode, clinical parameters	154 patients	HR	Different classifiers	Predicting TP53 mutation	Pathology	0.846	82.3	80.6	83.9
Liang 2025 [113]	Retrospective, single center	B-mode, CEUS, clinical variables	434 patients	HR	HR features from B-mode, arterial, portal venous, and delayed phases	CK-19 prediction	Pathology	0.927	86.9	89.3	86.3

Table 4

Table 5

Summary of published ultrasound radiomics studies for prognosis prediction of hepatocellular carcinoma"

Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	Results
Reference	Study design	Imaging modality	No. of patients/images	Radiomics technique	Detailed method	Task	Reference standard	AUC	C-index	Accuracy	Sensitivity	Specificity
ER, early recurrence; LR, late recurrence; RFS, recurrence free survival; MWA, microwave ablation; HR, handcrafted radiomics; DL, deep learning; CEUS, contrast-enhanced ultrasound; HCC, hepatocellular carcinoma; SR, surgical resection; RFA, radiofrequency ablation; PHLF, post-hepatectomy liver failure; ISGLS, international study group of liver surgery
Huang 2021 [114]	Retrospective, single center	B-mode, CEUS	215 patients	HR	Combined features from both tumoral and peritumoral area in different CEUS phase	ER prediction after resection or ablation	follow up	0.845		84.2	86.7	82.6
Cao 2024 [115]	Retrospective, single center	B-mode, clinical variables	127 patients	HR	Logistic regression	ER prediction following surgical resection	follow up	0.925			77.8	100
Wu 2022 [116]	Retrospective, single center	B-mode	513 patients	DL+HR	Comparison of DL and HR features	ER, LR, RFS after MWA and differentiation prediction	follow up		0.695 (ER); 0.715 (LR); 0.721 (RFS)
Zhang 2022 [117]	Retrospective, single center	B-mode, CEUS,	172 patients	DL+HR	Combination of DL and HR	ER prediction following surgical resection	follow up	0.889		78.4	90	66.7
Huang 2022 [118]	Retrospective, single center	B-mode, CEUS, clinical features	414 patients	DL+HR	Combination of both HR and DL features	ER and survival prediction after resection	follow up	0.57 (ER)	0.759 (OS)	59 (ER)	62 (ER)	56 (ER)
Ma 2021 [119]	Retrospective, single center	B-mode, SWE, clinical features	318 patients	DL+HR	Combining CEUS, US radiomics, and clinical factors	ER and LR prediction after ablation	follow up	0.84 (ER)	0.77 (LR)	81 (ER)	69 (ER)	93 (ER)
Liu 2020 [120]	Retrospective, single center	CEUS, clinical variables	419 patients	DL	Cross Stratification Using R-RFA and R-SR in Swapped Groups	PFS prediction of early HCC after SR and RFA	follow up	0.726 (RFA); 0.741 (SR)
Zhong 2023 [121]	Prospective, single center	SWE, clinical variables	345 patients	HR	multi-scale radiomics model	PHLF prediction	ISGLS	0.822		75	70.4	77.3
Xue 2024 [122]	Retrospective, multi-center	B-mode, CEUS, clinical features	532 patients	DL, HR	Resnet 50 trained with varying granularity with progressive training strategy	PHLF prediction	ISGLS	0.860-1		88-90.5	75-100	87.5-100
Jiang 2025 [123]	Retrospective, single center	SWE, clinical variables	633 patients	HR	Different classifier	Postoperative complications and ER prediction after resection	Comprehensive Complication Index (CCI) and follow up	0.832 (complications); 0.844 (ER)		78; 66.3	78.6; 60.4	74.6: 98.5
Liu 2019 [124]	Retrospective, single center	B-mode, CEUS	130 patients	DL, HR	Comparison of R-DLCEUS, R-TIC, and R-Bmode models	Prediction of responses to TACE	mRECIST	0.93 (R-DLCEUS)		90	89.3	92.3
Jin 2021 [125]	Retrospective, single center	B-mode, SWE, clinical features	434 patients	DL	Combination of 2D-SWE and B-mode DL features, sex and age	HCC occurrence in chronic hepatitis B patients	follow up	0.9			83.3	96.3

Table 5

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