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

doi:10.4251/wjgo.v14.i4.765

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World J Gastrointest Oncol. Apr 15, 2022; 14(4): 765-793
Published online Apr 15, 2022. doi: 10.4251/wjgo.v14.i4.765

Table 1 Artificial intelligence applications in the prevention of hepatocellular carcinoma

	First author	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance	Ref.
1	Wang J	Genetic and epigenetic biomarkers	Several	137 HCC and 431 non-HCC patients	HCC screening	0.910-0.950¹^,², 0.897-0.938¹^,³, 75.0-91.5²^,⁴, 66.4-90.6³^,⁴, 1.0-88.8²^,⁵, 0.5-87.9³^,⁵	[47]
2	Nam JY	Laboratory results, clinicopathological parameters	DNN	424/316³patients	HCC development in HBV cirrhosis	0.719¹^,², 0.782¹^,³	[48]
3	Xia Q	Long non-coding RNAs	Several	38 healthy samples, 45 chronic HBV patients, 46 liver cirrhosis, and 46 HCC patients	HCC development in HBV cirrhosis	71.1-89.5³^,⁶	[49]
4	Chen S	HBV reverse transcriptase gene sequencing	RF, SVM, KNN	307 chronic HBV patients (202/105), 237 HCC patients (159/78)	HCC development in HBV cirrhosis	RF: 0.902-0.903¹^,², 0.903-0.943¹^,³, SVM: 0.879-0.924¹^,², 0.727-0.858¹^,³, KNN: 0.680-0.737¹^,², 0.734-0.747¹^,³	[50]
5	Hashem S	Laboratory results, clinicopathological parameters	Several	3099 chronic HCV patients1324 HCC patients	HCC development in HCV cirrhosis	93.2-95.6³^,⁶, 0.955-0.990¹^,³, 86.3-91.8³^,⁴, 93.9-97.3³^,⁵	[51]
6	Audureau E	Laboratory results, clinicopathological parameters	Several	836/668⁷	HCC development in HCV cirrhosis	0.633-0.807¹^,², 0.623-0.715¹^,⁷	[52]
7	Ioannou GN	Clinical/laboratory data extracted directly from electronic health records	DNN	48151 patients with HCV-related cirrhosis (training:test = 9:1)	HCC development in HCV cirrhosis	0.759-0.806¹^,³	[53]
8	Singal AG	Laboratory results, clinicopathological parameters	RF	442/1050⁷	HCC development in cirrhosis	0.71¹^,², 0.64¹^,⁷	[54]

¹Area under the receiver operating curve or c-index.

²Training.

³Internal validation.

⁴Sensitivity (%).

⁵Specificity (%).

⁶Accuracy (%).

⁷External validation/testing.

CCA: Cholangiocarcinoma; CNN: Convolutional neural network; CT: Computed tomography; DNN: Deep neural network; HBV: Hepatitis B virus; HCC: Hepatocellular carcinoma; HCV: Hepatitis C virus; KNN: K-nearest neighbor; RF: Random forest; SVM: Support vector machine; WSI: Whole-slide image.

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	16500/4125²/3718⁶US 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	460/100⁶patients	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	20⁷/26²/80⁶WSIs	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	28/4²/30⁶WSIs	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	237/60⁶patients	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	75/40⁶HCCs	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.

Table 3 Artificial intelligence application in hepatocellular carcinoma treatment

	First author	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance	Ref.
1	Tsilimigras DI	Laboratory results, clinicopathological parameters, tumor characteristics	CART	976	Determining factors of prognostic weight preoperatively within the BCLC staging system	-	[99]
2	Liu F	Contrast-enhanced US radiomics, laboratory tests, and clinicopathological parameters	CNN	293/126 patients	2-yr progression-free survival of patients following RFA or surgical resection	0.754-0.784¹^,², 0.726-0.741¹^,³	[100]
3	Choi GH	Demographics, laboratory results, tumor characteristics, clinicopathological parameters	RF	813/208 patients	Treatment recommendation. Survival prediction	76.6-88.4³^,⁴, 53.0-82.3³^,⁵, 69.3-95.8³^,⁶. 0.676-0.959¹^,³	[101]
4	Chen M	Hematoxylin and eosin-stained WSI	CNN	377 (training:validation = 3:1)/ 67⁷ patients	Mutation prediction	89.6-94.0³^,⁴, 0.720-0.805¹^,⁷	[75]
5	Liao H	Hematoxylin and eosin-stained WSI	CNN	309/65³/78⁷	Mutation prediction	0.519-0.903¹^,³, 0.605-0.797¹^,⁷	[103]
6	Gu J	Multiphasic CT scans	CNN	14 patients	Mutation prediction	67.7-77.3³^,⁴	[104]
7	Chen G	Laboratory results	LIME	1007/1085⁷ patients	MVI	0.918¹^,², 0.832¹^,³, 0.905¹^,⁷	[105]
8	Zhang Y	MRI scans	CNN	158/79 patients	MVI	0.81¹^,², 69²^,⁵, 79²^,⁶, 0.72¹^,³, 55³^,⁵, 81³^,⁶	[106]
9	Wang G	DWI	CNN	60/40² HCCs	MVI	66.81-77.50²^,³^,⁴, 68.65-79.69¹^,²^,³, 56.56-76.47²^,³^,⁵, 64.35-79.13²^,³^,⁶	[107]
10	Liu QP	CT radiomics	RF, SVM	494 patients	MVI	0.84¹^,², 0.79¹^,³	[108]
11	Jiang YQ	CT radiomics, clinical/laboratory parameters	Gradient boosting, CNN	405 patients [220 MVI (+)/185 MVI (-)]	MVI	Gradient boosting: 0.900-0.952¹^,², 0.873-0.887¹^,³. CNN: 80.2-85.2³^,⁴, 0.900-0.980¹^,², 0.875-0.906¹^,³, 0.659-0.932³^,⁵, 0.757-0.973³^,⁶	[109]
12	Cucchetti A	Laboratory results, clinicopathological parameters, radiological data, histological data	ANN	175/75³	MVI. Histopathological grade	0.92¹^,², 91.0³^,⁴. 0.94¹^,², 93.3³^,⁴	[110]
13	Mai RY	Laboratory results, clinicopathological parameters, liver volumetry	ANN	265/88 patients	Posthemihepatectomy liver failure	0.880¹^,², 0.876¹^,³	[111]
14	Shi HY	Laboratory results, clinicopathological parameters, surgery parameters	ANN	22926 hepatectomies	In-hospital mortality following surgical resection	97.28³^,⁴, 0.84¹^,³, 95.93⁴^,⁷, 0.82¹^,⁷, 78.40⁵^,⁷, 94.57⁶^,⁷	[112]
15	Liu D	US radiomics	CNN	89/41 patients	Classify full/partial response from stable disease/ progression in patients treated with TACE	78-98²^,⁴, 0.82-0.98¹^,², 78.6-98.2²^,⁵, 74.2-96.7²^,⁶, 0.80-0.90³^,⁴, 0.80-0.93¹^,³, 82.1-89.3³^,⁵, 73.3-92.3³^,⁶	[113]
16	Morshid A	Multiphasic CT scans, BCLC stage	CNN, RF	105 patients	Classify TACE-susceptible from TACE-refractory HCC	62.9-74.2³^,⁴, 0.733¹^,³	[114]
17	Peng J	CT imaging	CNN	562/89⁷/138⁷	Classification of complete response, partial response, stable disease, and progressive disease following TACE	84.0²^,⁴, 0.95-0.97¹^,², 82.8-85.1⁴^,⁷, 0.94-0.98¹^,⁷	[115]
18	Abajian A	MRI imaging, clinical data	RF	36 patients	Classification of responders and non-responders following TACE	66³^,⁴, 62.5³^,⁵, 67.9³^,⁶	[116]
19	Zhu Y	FF-OCT	SVM	285 en face images	Cancerous hepatic cell identification	0.9378¹^,⁷	[117]
20	Liang Z	X-ray imaging	CNN	2943/1542³/1442⁷ images	Localization of fiducial markers	98.6⁴^,⁷	[118]
21	Liu Y	CT/MRI imaging	Dense-cycle GAN	21 patients	Identify differences between synthetic CT and CT, and compare their dose distribution	-	[119]
22	Taebi A	Computational fluid dynamics	CNN	3804 samples	Yttrium-90 distribution in radioembolization	Mean square error: 0.54 ± 0.14	[120]
23	Tong Z	DNA profiling	SVM	43 patients	Drug target prediction	0.8827-0.8849¹^,³, 53-65.44³^,⁵, 88.76-93.63³^,⁶	[121]

¹Area under the receiver operating curve or c-index.

²Training.

³Internal validation.

⁴Accuracy (%).

⁵Sensitivity (%).

⁶Specificity (%).

⁷External validation/ testing.

ANN: Artificial neural network; BCLC: Barcelona clinic liver cancer; CART: Classification and regression tree; CNN: Convolutional neural network; CT: Computed tomography; DWI: Diffusion-weighted imaging; FF-OCT: Full-field optical coherence tomography; GAN: Generative adversarial network; HCC: Hepatocellular carcinoma; LIME: Local Interpretable Model-agnostic Explanations; MRI: Magnetic resonance imaging; MVI: Microvascular invasion; RF: Random forest; RFA: Radiofrequency ablation; TACE: Transarterial chemoembolization; US: Ultrasound; WSI: Whole-slide image.

Table 4 Artificial intelligence application in hepatocellular carcinoma prognosis

	First author	Parameters employed	AI classifier	Sizes of the training/validation sets	Outcomes	Performance	Ref.
1	Chaudhary K	DNA methylation, RNA, and microRNA profiling	Several	360 patients (training:validation = 6:4)	Overall survival	0.70¹^,², 0.66-0.70¹^,³, 0.67-0.82¹^,⁴	[122]
2	Chicco D	50 laboratory and clinical parameters	Several	165 patients with HCC	Overall survival	RF: 77.2¹, 0.766⁵; Linear SVM: 77.1⁵, 0.763¹; MLP: 72.7⁵, 0.695¹; Radial SVM: 68.0⁵, 0.663¹; DT: 65.9⁵, 0.650¹	[123]
3	Liu X	Laboratory results, data from immunochemistry of peripheral blood mononuclear cells, tumor characteristics	GBA classifier	136/56³/105⁴	Risk of HCC-related death	0.844¹^,², 0.827¹^,³, 0.806¹^,⁴	[124]
4	Shi HY	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	22926 patients	5-yr survival following surgical resection	96.57³^,⁵, 0.885¹^,³, 97.43⁴^,⁵, 0.871¹^,⁴, 74.23⁴^,⁶	[125]
5	Chiu HC	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	434, 341, and 264 patients for 1-, 3-, and 5-year survival(training:validation = 8:2)	1-, 3-, and 5-yr overall survivalfollowing surgical resection	98.5-99.5²^,⁵, 0.980-0.993¹^,², 99.7-100²^,⁶, 96.2-99.2²^,⁷, 72.1-85.1³^,⁵, 0.798-0.875¹^,³, 71.4-88.6³^,⁶, 50.0-82.1³^,⁷	[126]
6	Qiao G	Laboratory results, clinicopathological parameters, tumor characteristics	ANN	362/181³/104⁴ patients	Survival following surgical resection	0.855¹^,², 80.00²^,⁶, 73.40²^,⁷, 0.832¹^,³, 78.67³^,⁶, 75.70³^,⁷, 0.829¹^,⁴, 77.42⁴^,⁶, 78.08⁴^,⁷	[127]
7	Guo L	RNA sequencing	RF	239/130 patients	Overall survival	89³^,⁵	[128]
8	Saillard C	Hematoxylin and eosin-stained WSI	CNN	309/342⁴ WSIs	Survival following surgical resection	0.75-0.78¹^,², 0.68-0.70¹^,⁴	[129]
9	Zhong BY	ALBI/CTP stage	ANN	548/115⁴/175⁴	Survival of patients treated with chemoembolization as monotherapy	ALBI-based: 0.799¹^,⁴, 0.700¹^,⁴; CTP-based: 0.729¹^,⁴, 0.802¹^,⁴	[130]
10	Zhong BY	ALBI/CTP stage	ANN	319/61⁴/124⁴	Survival of patients treated with chemoembolization and sorafenib	ALBI-based: 0.716¹^,⁴, 0.823¹^,⁴; CTP-based: 0.779¹^,⁴, 0.693¹^,⁴	[131]
11	Zhang L	CT scans, clinical features	CNN	120/81³ patients	Survival of patients treated with chemoembolization and sorafenib	0.717¹^,², 0.714¹^,³	[132]
12	Liu QP	CT radiomics, clinical parameters	DNN-DAE	243 patients	Overall survival following TACE	0.87-0.93¹^,³	[108]
13	Mähringer-Kunz A	Routine laboratory tests and clinicopathological parameters	ANN	125/57 patients	1-yr overall survival following TACE	0.77¹^,², 0.83¹^,³, 77.8³^,⁶, 81.0³^,⁷	[133]
14	Liu X	Routine laboratory tests and clinicopathological parameters	ANN	1480/637 patients	Progression-free survival. Overall survival	0.866¹^,², 0.730¹^,³. 0.877¹^,², 0.804¹^,³	[134]
15	Ho WH	Laboratory results, clinicopathological parameters, surgery parameters	ANN, DT	427, 354, and 297 patients for 1-, 3-, and 5-yr survival (training:validation = 8:2)	1-, 3-, and 5-yr disease-free survival following surgical resection	ANN: 0.963-0.989¹^,², 93.5-96.3²^,⁶, 91.6-97.9²^,⁷, 0.774-0.864¹^,³, 70.0-78.7³^,⁶, 54.2-92.7³^,⁷. DT: 0.675-0.825¹^,², 19.6-94.8²^,⁶, 45.8-97.9²^,⁷, 0.561-0.718¹^,³, 0-88.5³^,⁶, 37.5-96.4³^,⁷	[135]
16	Bedon L	DNA methylation profiling	RF-based	300/74 specimens	6-mo progression-free survival	67.1-80.6²^,⁵, 64.8-80.2⁴^,⁵	[136]
17	Schoenberg MB	Routine laboratory tests and clinicopathological parameters	RFS	127/53 patients	Disease-free survival following resection	0.766-0.788¹^,³	[137]
18	Wu CF	Laboratory tests and clinicopathological parameters, treatment data	ANN	252 patients(training:validation = 8:2)	1-yr and 2-yr disease-free survival following RFA	0.72-0.77¹^,³, 56.3-63.6³^,⁶, 70.0-71.8³^,⁷	[138]
19	Divya R	Laboratory results, clinicopathological parameters, tumor characteristics	APO, SVM, RF	152 patients	Recurrence following RFA	95.5³^,⁵, 95.1³^,⁶, 95.8³^,⁷	[139]
20	Huang Y	Demographics, laboratory tests, tumor characteristics	GBS classifier	5928/1483 patients	Recurrence following surgical resection. Overall survival	0.704¹^,², 0.697-0.713¹^,³. 0.565-0.736¹^,², 0.551-0.751¹^,³	[140]
21	Shen J	Disease-free related genes sequencing	DT, SVM	315 HCC patients	Recurrence following surgical resection	DT: 74.19⁵, 0.75¹, 70.41⁴^,⁵. SVM: 80.65⁵, 0.595¹	[141]
22	Wang W	CT radiomics, clinical data	CNN, SVM, RF	167 patients	Early recurrence following surgical resection	0.723-0.825¹^,³	[142]
23	Ji GW	CT radiomics, laboratory results, clinicopathological parameters	Several	210/107³/153⁴ patients	Recurrence time following surgical resection	Radiomics model: 0.748-0.752¹^,², 0.781-0.801¹^,³, 0.733-0.741¹^,⁴. Clinical model: 0.716-0.727¹^,², 0.707-0.739¹^,³, 0.696-0.716¹^,⁴	[143]
24	Xu D	Routine laboratory tests and clinicopathological parameters, intra-operative parameters	BN-based	995 patients	Recurrence time following surgical resection	0.57³^,⁵, 0.57³^,⁶	[144]
25	Jianzhu B	Several including immune, tumor, nutrition, and indicators	CS-SVM	776 liver cancer recurrences	Recurrence time. Recurrence location	Mean square error = 9.2101, 95.7⁵, 0.95¹	[145]
26	Yamashita R	Hematoxylin and eosin-stained WSI	CNN	299/53³/198⁴ WSIs	Recurrence following surgical resection	0.724¹^,³, 0.683¹^,⁴	[77]
27	Liao H	Hematoxylin and eosin-stained WSI	RF	491 WSIs	Overall survival	0.563-0.706¹^,³, 0.565-0.621¹^,⁴	[146]
28	Saito A	Hematoxylin and eosin-stained WSI	SVM	69/89⁴	Recurrence time following surgical resection	99.8²^,⁵, 68.1-80.6⁴^,⁵	[147]
29	Liang JD	Laboratory results, clinicopathological parameters	SVM	83 patients	Recurrence following RFA	73-82³^,⁵, 0.60-0.69¹^,³, 77-86³^,⁶, 73-82³^,⁷	[148]
30	An C	MRI scans	CNN	141 HCC lesions	Local tumor progression following MWA	0.728¹	[149]
31	Nam JY	Routine laboratory tests and clinicopathological parameters	DNN	349/214 patients	Post-transplant HCC recurrence	0.62-0.75¹^,³, 0.63-0.76³^,⁶, 0.46-0.62³^,⁷	[150]
32	Nam JY	Laboratory results, clinicopathological parameters, tumor characteristics	DNN	349/214 transplanted patients	Post-transplant HCC recurrence	0.75¹^,³, 76³^,⁶, 46³^,⁷	[150]
33	Rodriguez-Luna H	Genotyping data from microsatellite mutations/deletions	ANN	19 transplanted patients	Post-transplant HCC recurrence	89.5³^,⁵	[151]
34	Guo D	Laboratory results, clinicopathological parameters CT radiomics	LASSO	93/40 transplanted patients	Recurrence free-survival following liver transplantation	0.675-0.785¹^,², 0.705-0.789¹^,³	[152]
35	Lau L	Laboratory results, clinicopathological parameters, donor characteristics	ANN, RF	90/90 transplants	Graft failure/primary nonfunction. 3-mo graft failure	ANN: 0.734-0.835¹^,³; RF: 0.787-0.818¹^,³. ANN: 0.559¹^,³, RF: 0.715¹^,³	[153]
36	Briceño J	Laboratory results, clinicopathological parameters, surgical parameters, donor characteristics	ANN	1003 liver transplants	3-mo graft failure	0.806-0.821¹^,³	[154]
37	Ershoff BD	Laboratory results, clinicopathological parameters, donor characteristics	DNN	46035/11509	90-d post-transplant survival	0.695-0.708¹^,³, 30.9-35.8³^,⁶, 88.1-90.8³^,⁷	[155]

¹Area under the receiver operating curve or c-index.

²Training.

³Internal validation.

⁴External validation/testing.

⁵Accuracy (%).

⁶Sensitivity (%).

⁷Specificity (%).

ALBI: Albumin-bilirubin; ANN: Artificial neural network; APO: Artificial plant optimization; BN: Bayesian network; CNN: Convolutional neural network; CS: Cuckoo-search; CT: Computed tomography; CTP: Child-Turcotte-Pugh; DNN: Deep neural network; DAE: Deep auto-encoder; DT: Decision tree; GBS: Gradient boosting survival; HCC: Hepatocellular carcinoma; LASSO: Least absolute shrinkage and selection operator; MLP: Multi-layer perceptron neural network; MRI: Magnetic resonance imaging; MWA: Microwave ablation; RF: Random forest; RFA: Radiofrequency ablation; SVM: Support vector machine; TACE: Transarterial chemoembolization; US: Ultrasound; WSI: Whole-slide image.

Citation: Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793
URL: https://www.wjgnet.com/1948-5204/full/v14/i4/765.htm
DOI: https://dx.doi.org/10.4251/wjgo.v14.i4.765