Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Table 1 Artificial intelligence applications in gastric cancer pathology

Ref.	Task	No. of cases/data set	Machine learning method	Performance
Bollschweiler et al[79]	Prognosis prediction	135 cases	ANN	Accuracy (93%)
Duraipandian et al[80]	Tumor classification	700 slides	GastricNet	Accuracy (100%)
Cosatto et al[65]	Tumor classification	> 12000 WSIs	MIL	AUC (0.96)
Sharma et al[21]	Tumor classification	454 cases	CNN	Accuracy (69% for cancer classification), accuracy (81% for necrosis detection)
Jiang et al[81]	Prognosis prediction	786 cases	SVM classifier	AUCs (up to 0.83)
Qu et al[82]	Tumor classification	9720 images	DL	AUCs (up to 0.97)
Yoshida et al[23]	Tumor classification	3062 gastric biopsy specimens	ML	Overall concordance rate (55.6%)
Kather et al[34]	Prediction of microsatellite instability	1147 cases (gastric and colorectal cancer)	Deep residual learning	AUC (0.81 for gastric cancer; 0.84 for colorectal cancer)
Garcia et al[30]	Tumor classification	3257 images	CNN	Accuracy (96.9%)
León et al[83]	Tumor classification	40 images	CNN	Accuracy (up to 89.7%)
Fu et al[32]	Prediction of genomic alterations, gene expression profiling, and immune infiltration	> 1000 cases (gastric, colorectal, esophageal, and liver cancers)	Neural networks.	AUC (0.9) for BRAF mutations prediction in thyroid cancers
Liang et al[84]	Tumor classification	1900 images	DL	Accuracy (91.1%)
Sun et al[85]	Tumor classification	500 images	DL	Accuracy (91.6%)
Tomita et al[24]	Tumor classification	502 cases (esophageal adenocarcinoma and Barret esophagus)	Attention-based deep learning	Accuracy (83%)
Wang et al[86]	Tumor classification	608 images	Recalibrated multi-instance deep learning	Accuracy (86.5%)
Iizuka et al[22]	Tumor classification	1746 biopsy WSIs	CNN, RNN	AUCs (up to 0.98), accuracy (95.6%)
Kather et al[33]	Prediction of genetic alterations and gene expression signatures	> 1000 cases (gastric, colorectal, and pancreatic cancer)	Neural networks	AUC (up to 0.8)

ANN: Artificial neural network; GastricNet: The deep learning framework; WSIs: Whole slide images; MIL: Multi-instance learning; AUC: Area under the curve; CNN: Convolutional neural networks; SVM: Support vector machine; DL: Deep learning; ML: Machine learning; RNN: Recurrent neural networks.