Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives

Table 3 Previous studies on the pathology of gastric cancer using artificial intelligence

Ref.	Targets	Sample size	Input	Task	Analysis method	Diagnostic performance
Qu et al[39]	GC	15000 images	Pathological images	Evaluation of stepwise methods	CNN	AUC: 0828-0.920
Yoshida et al[40]	GC	3062 biopsy samples	Pathological images stained by H&E	Automatic segmentation, diagnosis of carcinoma	CNN	Sensitivity: 89.5%, specificity: 50.7%
Mori et al[41]	GC (surgery)	516 images from 10 GC cases	Pathological images stained by H&E	Diagnosis of invasion depth in signet cell carcinoma	CNN	Sensitivity: 90%, Specificity: 81%
Jiang et al[42]	GC (surgery)	786 cases	IHC (CD3, CD8, CD45RO, CD45RA, CD57, CD68, CD66b, and CD34)	Prediction of survival	SVM	The immunomarker SVM was useful for predicting survival

GC: Gastric cancer; AUC: Area under the curve; H&E: Hematoxylin eosin staining; CNN: Convolutional neural network; IHC: Immunohistochemistry; SVM: Support vector machine.