Artificial intelligence in gastric cancer: Application and future perspectives

Table 1 Applications of artificial intelligence in endoscopy based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Liu et al[7]	2016	China	400 images	Hospital	JDPCA	AUCs (0.9532), accuracy (90.75%)
Ali et al[8]	2018	Pakistan	176 images	Public images dataset	G2LCM	AUC (0.91), accuracy (87%)
Luo et al[9]	2019	China	1036496 images	Hospital	GRAIDS	Accuracy (up to 97.7%)
Sakai et al[10]	2018	Japan	29037 images	Hospital	CNN	Accuracy (87.6%)
Yoon et al[11]	2019	South Korea	11539 images	Hospital	VGG model	AUCs (0.981 for detection), AUCs (0.851 for depth prediction)
Nakahira et al[12]	2019	Japan	107284 images	Cancer Institute	Deep neural network	Kappa value (0.27)
Zhu et al[13]	2019	China	993 images	Hospital	CNN-CAD system	AUCs (0.94), accuracy (89.16%)
Wang et al[14]	2019	China	104864 images	Hospital	MCNN	Sensitivity (79.622%), specificity (78.48%)
Guimarães et al[15]	2020	Germany	270 images	Medical center	DL	AUCs (0.98), accuracy (93%)
Miyaki et al[16]	2015	Japan	100 cases	Hospital	SVM	Average output value (0.846 ± 0.220)
Liu et al[17]	2018	China	1120 M-NBI images/3068 images	Hospital	Deep CNN	Top accuracy (98.5%)
Horiuchi et al[18]	2019	Japan	2828 images	Hospital	CNN	Accuracy (85.3%)
Li et al[19]	2019	China	2088 images	Hospital	CNN	Accuracy (90.91%)
Bergholt et al[20]	2011	Singapore	1063 in vivo Raman spectra	Hospital	ACO-LDA algorithms	Sensitivity (94.6%), specificity (94.6%)
Duraipandian et al[21]	2012	Singapore	2748 in vivo Raman spectra	Hospital	PLS-DA algorithms	Accuracy (85.6%), specificity (86.2%)

JDPCA: Joint diagonalization principal component analysis; AUC: Area under the curve; G2LCM: Gabor-based gray-level co-occurrence matrix; GRAIDS: Gastrointestinal Artificial Intelligence Diagnostic System; CNN: Convolutional neural network; VGG model: Visual geometry group; CNN-CAD system: Convolutional neural network computer-aided detection; MCNN: Multicolumn convolutional neural network; DL: Deep learning; SVM: Support vector machine; M-NBI: Narrow-band imaging; ACO-LDA: Ant colony optimization integrated with linear discriminant analysis; PLS-DA: Partial least squares-discriminant analysis.

Table 2 Applications of artificial intelligence in pathology and computerized tomography based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Li et al[22]	2018	China	700 slices	Publicly gastric slice dataset	GastricNet	Accuracy (100%)
Sharma et al[23]	2017	Germany	454 cases	Hospital	CNN	Accuracy (0.6990 for cancer classification), accuracy (0.8144 for necrosis detection)
Leon et al[24]	2019	Colombia	40 images	Department of pathology	Deep CNN	Accuracy (up to 89.72%)
Iizuka et al[25]	2020	Japan	1746 biopsy histopathology WSIs	Hospital, TCGA	CNN, RNN	AUCs (up to 0.98), accuracy (95.6%)
Yoshida et al[26]	2018	Japan	3062 gastric biopsy specimens	Cancer center	ML	Overall concordance rate (55.6%)
Garcia et al[27]	2017	Peru	3257 images	-	Deep CNN	Accuracy (96.88%)
Liang et al[28]	2019	China	1900 images	-	DL	IoU (0.883), accuracy (91.09%)
Qu et al[29]	2018	Japan	9720 images/19440 images	Hospital	DL	AUCs (up to 0.965)
Sun et al[30]	2019	China	500 pathological images	Hospital	DL	IoU (0.8265), accuracy (91.60%)
Cao et al[31]	2019	China	1399 pathological sections	-	the Mask R-CNN	AP value (61.2)

GastricNet: The deep learning framework; CNN: Convolutional neural networks; WSIs: Whole slide images; TCGA: The Cancer Genome Atlas; RNN: Recurrent neural networks; AUC: Area under the curve; ML: Machine learning; DL: Deep learning; IoU: Intersection over union coefficient; AP: Average precision.

Table 3 Applications of artificial intelligence in computerized tomography based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Huang et al[32]	2020	China	-	Hospital	Deep CNN	-
Gao et al[33]	2019	China	32495 images	Hospital	FR-CNN	AUCs (0.9541)
Li et al[34]	2015	China	26 cases	Hospital	KNN algorithm	Accuracy (76.92%)
Li et al[35]	2012	China	38 lymph node datasets	Hospital	ML	Accuracy (96.33%)

CNN: Convolutional neural networks; FR-CNN: Faster region-based convolutional neural networks; AUCs: Area under the curve; KNN algorithm: K-nearest neighbor algorithm; ML: Machine learning.

Table 4 Applications of artificial intelligence in gastric cancer prognosis based on different study population

Ref.	Year	Country/region	Number of cases	Study population	Methods	Results
Jiang et al[36]	2018	China	786 cases	Hospital	SVM classifier	AUCs (up to 0.834)
Lu et al[37]	2017	China	939 patients	Hospital	MMHG	Accuracy (69.28%)
Korhani Kangi et al[38]	2018	Iran	339 patients	Hospital	ANN, BNN	Sensitivity (88.2% for ANN, 90.3% for BNN), specificity (95.4% for ANN, 90.9% for BNN)
Zhang et al[39]	2019	China	669 cases	Hospital	ML	AUCs (up to 0.831)
Liu et al[40]	2018	China	432 GC tissue samples	Hospital	SVM classifier	Accuracy (up to 94.19%)
Bollschweiler et al[41]	2004	Germany, Japan	135 cases	Cancer center	ANN	Accuracy (93%)
Hensler et al[42]	2005	Germany, Japan	4302 cases	Cancer center	QUEEN technique	Accuracy (72.73%)
Jagric et al[43]	2010	Slovenia	213 cases	Clinical center	Learning vector quantization neural networks	Sensitivity (71%), specificity (96.1%)

SVM: Support vector machine; AUC: Area under the curve; MMHG: Multimodal hypergraph learning framework; ANN: Artificial neural networks; BNN: Bayesian neural network models; ML: Machine learning; GC: Gastric cancer; QUEEN technique: Quality assured efficient engineering of feedforward neural networks with supervised learning.