Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities

Table 2 Artificial intelligence application in hepatocellular carcinoma diagnosis

	First author	Diagnostic modality	AI classifier	Sizes of the training/validation sets	Outcomes	Performance	Ref.
1	Sato M	Laboratory results, clinicopathological parameters	Several	1582 patients	HCC early detection	81.65-87.361,2, 0.870-0.9403,2	[55]
2	Zhao X	MicroRNA expression profiles	Several	392 patients	HCC early detection	RF: 0.9823, SVM: 0.9703, DT: 0.8313	[56]
3	Zhang ZM	Gene expression profiles	SVM	1333/336 HCC samples	HCC early detection	1001,2, 1002,4, 1002,5, 0.95973,6, 91.934,6, 1005,6	[57]
4	Tao K	Circulating tumor DNA	RF-based	209/766/996	HCC early detection	0.874-0.9331,2, 0.812-0.9203,6	[58]
5	Li G	MicroRNA and long non-coding RNA expression profiles	SVM, RF, DT	361 patients	HCC early detection	RF: 0.9921,2, 95.62,4, 1002,5; SVM: 0.9922,3, 97.22,4, 98.02,5; DT: 0.9272,3, 98.32,4, 92.02,5	[59]
6	Schmauch B	US imaging	CNN	109 images with focal liver lesions	Classification of benign from malignant focal liver lesions; classification among five focal liver lesions	0.916-0.9422,3; 0.886-0.9542,3	[60]
7	Yang Q	US imaging, clinical parameters	CNN	16500/41252/37186 US images	Classification among 16 different focal liver lesions	0.859-0.9663,7, 0.765-0.9252,3, 0.750-0.9243,6	[61]
8	Virmani J	B-mode US imaging	NNE	108 images	Classification among normal liver and four focal liver lesions	95.01,2	[62]
9	Shiraishi J	Microflow imaging of contrast-enhanced US	ANN	103 focal liver lesions	Classification among HCC, metastasis, and hemangioma; histopathological grade	86.9-93.81,2; 50.0-79.21,2	[63]
10	Zhou J	Multiphasic CT scans	CNN	616 liver lesions	Classification of benign and malignant lesions. Classification of 6 types of focal liver lesions	76.6-88.42,4,5, 82.51,2, 0.9212,3, 46.4-93.12,4, 91.9-98.62,5, 73.41,2, 0.766-0.9832,3	[64]
11	Yasaka K	Contrast-enhancedCT imaging	CNN	460/1006 patients	Classification among five types of focal liver lesions	951,7, 841,6, 33-1004,6	[65]
12	Shi W	Multiphasic CT scans	MP-CDN	449 focal lesions. Training:validation ratio = 8:2	Classification between HCC and non-HCC focal lesions	0.811-0.8561,2, 0.862-0.9252,3, 0.744-0.9232,4, 0.725-0.9412,5	[66]
13	Todoroki Y	Multiphasic CT imaging	CNN	89 patients	Classification among five focal liver lesions	79-1002,4	[67]
14	Matake K	Clinicopathological parameters, CT imaging	ANN	120 patients	Classification among four types of focal liver lesions	0.9612,3	[68]
15	Liang W	CT and MRI radiomics	RF	170 CT scans; 137 MRI scans	Classification of three types of focal liver lesions	CT model: 0.9963,7, 0.8792,3. MRI model: 0.9993,7, 0.9252,3	[69]
16	Hamm CA	Multiphasic MRI imaging	CNN	434/60 lesions	Classification among six types of focal liver lesions; identify HCC; classification of LI-RADS	922,4, 982,5; 0.9922,3; 944,6, 972,5	[70,71]
17	Jansen MJA	MRI imaging	Extremely randomized trees classifier	95 patients	Classification among five different focal liver lesions	85-921,2, 62-932,4, 56-932,5	[72]
18	Zhen SH	MRI scans	CNN	1210/2016	Classification among seven different focal liver lesions	0.841-0.9873,6, 40.5-1004,6, 86.4-99.55,6	[73]
19	Kiani A	Hematoxylin and eosin-stained WSI	CNN	207/262/806 WSIs	Classification of HCC and CCA	88.51,2, 84.21,6	[74]
20	Chen M	Hematoxylin and eosin-stained WSI	CNN	491 WSIs (402 HCC, 89 normal liver tissue)	Classification of HCC and normal liver tissue; histopathological grade	0.9601,2, 0.9612,3; 89.61,2	[75]
21	Lin H	Multiphoton microscopy	CNN	217 images	Histopathological grade	0.812-0.9411,2, 0.891-0.9172,3	[76]
22	Yamashita R	Hematoxylin and eosin-stained WSI	CNN	28/42/306 WSIs	HCC lesion detection	0.9522,3, 0.9563,6	[77]
23	Roy M	Hematoxylin and eosin-stained WSI	CAE	50 WSIs	Segmentation of viable tumors	91-951,2	[78]
24	Giordano S	PESI-MS	SVM, RF	117 HCCs, 50 CCA, 151 non-tumor group	Classification of HCC, CCA, and non-tumor groups	SVM: 95.1-98.51,6; RF: 94-94.91,6	[79]
25	Guo LH	Contrast-enhanced ultrasound imaging	MKL	93 lesions	Classification of benign from malignant focal liver lesions	90.411,2, 93.562,4, 86.892,5	[80]
26	Bharti P	US imaging	Several	189 images	Classify among normal liver, chronic liver disease, cirrhosis, and HCC	96.61,2, 95.5-96.92,4, 98.0-99.82,5	[81]
27	Brehar R	US imaging	CNN	268 patients	Classification between HCC and cirrhotic parenchyma	84.84-911,2, 0.91-0.952,3, 86.79-94.372,4, 82.95%-88.38%2,5	[82]
28	Mao B	Ultrasound radiomics	Several	114 patients	Classify primary from metastatic liver cancer	0.729-0.8081,2, 0.737-0.7932,3, 0.775-0.8682,4, 0.667-0.8802,5	[83]
29	Almotairi S	CT imaging	CNN	20 CT scans	Tumor segmentation	98.81,7	[84]
30	Budak Ü	CT imaging	CNN	20 CT scans	Tumor segmentation	Volumetric overlap error: 9.05%2	[85]
31	Nayak A	Multiphasic CT imaging	SVM	40 patients	Classification between HCC and cirrhotic parenchyma	80-86.91,2, 0.932,3	[86]
32	Krishan A	CT scans	Several	1638 CT scans	Identification of liver lesions; classification between HCC and metastasis	98.39-1001,2, 0.99-1.002,3; 76.38-87.011,2, 0.77-0.992,3	[87]
33	Chen WF	CT scans	SED	300 CT scans	Tumor segmentation	0.9921, 0.952,3	[88]
34	Khan AA	CT scans	Several	179 patients	Classification between HCC and hemangioma	96.6-98.31,6, 0.94-0.973,6, 94.23-97.035,6	[88]
35	Mokrane FZ	Multiphasic CT radiomics	Several	106/362/366	Classification between HCC and non-HCC lesions	0.813,7, 0.814,7, 0.725,7, 0.722,3, 0.663,6	[90]
36	Mao B	CT radiomics, clinical parameters	Gradient boosting	237/606 patients	Histopathological grade	61.18-97.051,6, 0.7071-0.99643,7, 60.67-95.514,7, 51.35-80.415,7, 48.33-70.001,6, 0.6128-0.80143,6, 43.48-65.224,6, 37.84-81.085,6	[91]
37	Preis O	PET/CT imaging	ANN	98 patients	Classification between benign and malignant liver lesions	0.896-0.9052,3	[92]
38	Trivizakis E	Diffusion-weighted MRI	CNN, SVM	134 patients	Classification between primary liver cancer and metastasis	85.51,7, 831,2, 0.802,3, 932,4, 672,5	[93]
39	Oestmann PM	Multiphasic MRI scans	CNN	150/102	Classification of HCC and non-HCC lesions	94.11,7, 87.31,2, 0.9122,3. For HCC: 92.72,4, 82.02,5. For non-HCC: 82.02,4, 92.72,5	[94]
40	Bousabarah K	MRI scans	CNN, RF	174 patients/ 231 lesions	HCC detection	0.66-0.752,4, 0.55-0.734,6	[95]
41	Kim J	MRI scans	CNN	4552,7/546	HCC detection	0.972,3, 942,4, 992,5, 0.903,6, 874,6, 935,6	[96]
42	Jian W	Non-enhanced MRI scans	CNN	75/406 HCCs	HCC detection	65.00-77.001,6, 0.70-0.823,6, 64.55-78.184,6, 65.56-75.565,6	[97]
43	Wu Y	Multiphasic MRI imaging	CNN	89 HCCs	Classification between LI-RADS 3 and LI-RADS 4/5	0.767-0.9001,6, 0.90-0.953,6, 0.76-1.004,6, 0.633-0.8075,6	[98]

¹Accuracy (%).

²Internal validation.

³Area under the receiver operating curve or c-index.

⁴Sensitivity (%).

⁵Specificity (%).

⁶External validation/testing.

⁷Training.

ANN: Artificial neural network; CAE: Convolutional autoencoder; CCA: Cholangiocarcinoma; CNN: Convolutional neural network; CT: Computed tomography; DNN: Deep neural network; DT: Decision tree; HCC: Hepatocellular carcinoma; LI-RADS: Liver imaging reporting and data system; MKL: Multiple kernel learning; MP-CDN: Multiphase convolutional dense networks; MRI: Magnetic resonance imaging; NGS: Next-generation sequencing; NNE: Neural network ensemble; PESI-MS: Probe electrospray ionization mass spectrometry; PET: Positron emission tomography; RF: Random forest; SED: Successive Encoder-Decoder; SVM: Support vector machine; US: Ultrasound; WSI: Whole-slide image.