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

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.