Artificial intelligence in pathological evaluation of gastrointestinal cancers

Table 2 Artificial intelligence-based applications in gastric cancer

Ref.	Task	No. of cases/data set	Method	Performance
Duraipandian et al[89]	Classification	700 slides	GastricNet	Accuracy (100%)
Cosatto et al[72]		> 12000 WSIs	MIL	AUC (0.96)
Sharma et al[31]		454 cases	CNN	Accuracy (69%)
Qu et al[90]		9720 images	DL	AUCs (up to 0.97)
Yoshida et al[32]		3062 gastric biopsies	ML	Overall concordance rate (55.6%)
León et al[91]		40 images	CNN	Accuracy (up to 89.7%)
Liang et al[92]		1900 images	DL	Accuracy (91.1%)
Sun et al[93]		500 images	DL	Accuracy (91.6%)
Tomita et al[94]		502 images1	Attention-based DL	Accuracy (83%)
Wang et al[95]		608 images	Recalibrated multi-instance-DL	Accuracy (86.5%)
Iizuka et al[33]		1746 biopsy WSIs	CNN, RNN	Accuracy (95.6%), AUCs (up to 0.98)
Bollschweiler et al[41]	Prognosis	135 cases	ANN	Accuracy (93%)
Hensler et al[42]		4302 cases	QUEEN technique	Accuracy (72.73%)
Jagric et al[43]		213 cases	Learning vector quantization NN	Sensitivity (71%), specificity (96.1%)
Lu et al[36]		939 cases	MMHG	Accuracy (69.28%)
Jiang et al[37]		786 cases	SVM classifier	AUCs (up to 0.83)
Liu et al[40]		432 tissue samples	SVM classifier	Accuracy (up to 94.19%)
Korhani Kangi and Bahrampour[38]		339 cases	ANN, BNN	Sensitivity (88.2% for ANN, 90.3% for BNN)Specificity (95.4% for ANN, 90.9% for BNN)
Zhang et al[39]		669 cases	ML	AUCs (up to 0.831)
García et al[44]	Tumor infiltrating lymphocytes	3257 images	CNN	Accuracy (96.9%)
Kather et al[56]	Genetic alterations	1147 cases2	Deep residual learning	AUC (0.81 for gastric cancer)
Kather et al[47]		> 1000 cases3	NN	AUC (up to 0.8)
Fu et al[57]		> 1000 cases4	NN	Variable across tumors/gene alterations. Strongest relations in whole genome duplications

¹Esophageal adenocarcinoma and Barrett’s esophagus.

²Gastric and colorectal cancers.

³Gastric, colorectal, esophageal, and liver cancers.

⁴Gastric, colorectal, and pancreatic cancers.

AI: Artificial intelligence; GastricNet: The deep learning framework; WSIs: Whole slide images; MIL: Multi-instance learning; AUC: Area under the curve; CNN: Convolutional neural networks; DL: Deep learning; ML: Machine learning; RNN: Recurrent neural networks; ANN: Artificial neural network; QUEEN technique: Quality assured efficient engineering of feedforward neural networks with supervised learning; NN: Neural network; MMHG: Multimodal hypergraph learning framework; SVM: Support vector machine.